Patent Publication Number: US-2009219379-A1

Title: Average Calculation in Color Space, Particularly for Segmentation of Video Sequences

Description:
FIELD OF THE INVENTION 
     The present invention generally relates to the field of digital image processing. 
     DESCRIPTION OF RELATED ART 
     In digital image processing, several different color models are used to define the information about the color and luminosity of the pixels. 
     For example, one such model is the RGB (acronym for Red, Green and Blue): for each pixel, three information elements (e.g., three bytes) define the value of a respective color component which, mixed with the other two components, define the pixel color and luminosity. 
     Another color model is the HSV (acronym for Hue, Saturation and Value). This format is sometimes preferred to the RGB representation, because the RGB color space is not perceptually uniform: in the RGB color space, numerically equal color differences in different colors are not perceived by the human eye as equal differences when the different colors are displayed; for example, if the green component is varied, the perceived color change is much more evident than in the case the blue component is varied of a same amount. 
     Differently, the metric in the HSV color space is essentially the same as that adopted by the human eye, so that working in the HSV color space produces better video segmentation results. 
     In greater detail, the S and V coordinates are linear coordinates, and their values typically range from 0 to 1; the coordinate H is instead an angular coordinate whose value ranges from 0° to 3600. A conventional graphical representation of the HSV space is in term of a cone turned upside-down, with the apex at the bottom and the base at the top; the cone axis is the axis of the V coordinate; the axis of the S coordinate is perpendicular to the V axis; the H coordinate indicates an angle formed with the S axis by a segment lying in a plane containing the S axis and orthogonal to the V axis, and starting from the origin of the V and S axis. In such a graphical representation, the dark colors, having low luminosity, are close to the bottom of the cone (close to the apex, which corresponds to black); the primary colors (and, in general, the saturated colors) correspond to points located on the cone surface, and become brighter and brighter moving along the V axis, from the apex towards the base; the low-saturated colors, tending to gray, are located within the cone close to the V axis, whereas the points of the V axis correspond to gray tones, and white is at the top of the V axis. From this graphical representation, it is possible to appreciate that the HSV space better describes the human eye&#39;s operation: where it is more difficult for the human eye to distinguish different colors, i.e. where the luminosity is scarce, the points of the HSV space are closer to each other (i.e., they are hardly distinguishable by comparison with a threshold); on the contrary, the brighter colors, especially the saturated ones, are more clearly distinguishable, and in fact the corresponding points in the HSV space are far from each other (i.e., they are easily distinguishable by comparison with a threshold). 
     Use of the HSV has for example been suggested in the field of digital video segmentation, wherein an input video stream is separated into two different streams, one containing foreground subjects/objects (for the purposes of the present invention, from now on by “foreground subject” it will be intended both foreground subjects, and foreground objects), and the other containing the background of the video frames. In a videocommunication (e.g. videotelephony) sequence between two persons, the foreground is for example represented by a talking person, usually limitedly to the trunk, the head and the arms (a so-called “talking head”). 
     The possibility of segmenting a video sequence into foreground and background streams is for example useful for changing the video sequence background, removing the original background and inserting a substitutive background of users&#39; choice, for instance to hide the talking head surroundings, for reasons of privacy, or to share video clips, movies, photographs, TV sequences while communicating with other persons, and similar applications. 
     The aim of many segmentation algorithms is to analyze a digital video sequence and to generate a binary mask wherein every pixel of every video frame of the video sequence is marked as either a background or a foreground pixel. In applications like videocommunication, the above operation has to be performed in real time, at a frame rate that, in a sufficiently fluid videocommunication sequence, is of the order of 25 to 30 frames per second (fps). 
     A review of algorithms for segmentation of color images is provided in L. Lucchese and S. K. Mitra, “Color Image Segmentation: A State-of-the-Art Survey” Proc. of the Indian National Science Academy (INSA-A), New Delhi, India, Vol. 67, A, No. 2, March 2001, pp. 207-221. 
     In A. R. J. François and G. G. Medioni, “Adaptive Color Background Modeling for Real-time Segmentation of Video Streams,” Proceedings of the International Conference on Imaging Science, Systems, and Technology, pp. 227-232, Las Vegas, NA, June 1999, a system is presented to perform realtime background modeling and segmentation of video streams on a Personal Computer (PC), in the context of video surveillance and multimedia applications. The images, captured with a fixed camera, are modeled as a fixed or slowly changing background, which may become occluded by mobile agents. The system learns a statistical color model of the background, which is used for detecting changes produced by occluding elements. It is proposed to operate in the Hue-Saturation. Value (HSV) color space, instead of the traditional RGB (Red, Green, Blue) space, because it provides a better use of the color information, and naturally incorporates gray-level only processing. At each instant, the system maintains an updated background model, and a list of occluding regions that can then be tracked. 
     In S. Lefevre et al., “Multiresolution Color Image Segmentation Applied to Background Extraction in Outdoor Images”, RFAI Publication, IS&amp;T European Conference on Color in Graphics, Image and Vision, Poitiers (France), April 2002, pp. 363-367, an adaptive technique for color image segmentation is presented. The segmentation is performed using a multiresolution scheme and considering the background areas have quite uniform color features at a low-resolution representation of the image. First, a pyramidal representation of the original image is built. Then segmentation is improved iteratively at each resolution using color information. The method allows to extract background areas in outdoor images. Several color spaces are compared in order to determine a robust method especially with respect to illumination changes which frequently occur in outdoor images. Mean value of Hue component from HSV color space is selected as the best decision criterion for image matching. Segmentation results of color image from soccer game video sequences are presented to illustrate the method efficiency. 
     SUMMARY OF THE INVENTION 
     The use of HSV representation of the pixel color and luminosity, even though superior to other representations in several applications, poses a problem in all those cases where averages of the pixel values need to be calculated. 
     The problem stems from the fact that the H coordinate is an angular coordinate; the Applicant has observed that, due to this fact, the calculation of the average value and of the variance for the H angular coordinate cannot proceed in the same way as for the linear coordinates S and V. Just by way of example, let it be assumed that two pixels have respective H coordinates equal to 1° and 359°, i.e., in the graphical representation of the HSV space, two points very close to the S positive half-axis (due to angle periodicity, the point having H=359° corresponds to the point having H=−1°, and is thus as close to the point having H=0° as the point having H=1°): the arithmetic average would be 180°, which however is totally incorrect, because corresponds to a point located on the S negative half-axis. As another example, let it be assumed that two pixels have respective H coordinates equal to 0° and 180°: the arithmetic average would be 90°, which is totally arbitrary. It is observed that in the latter example, a more correct average H coordinate should be indeterminate, i.e. corresponding to a point on the V axis. 
     In the framework of image processing, and particularly of segmentation of video sequences, the Applicant has tackled the problem of devising a method for reliably calculating the average value of an angular component (or coordinate) of a color space representation of the color and luminosity of the pixels, such as the hue component (or coordinate) in a HSV space color representation. 
     The Applicant has found that a reliable average value calculation for an angular coordinate can be performed by determining, from the angular coordinate, derived linear coordinates (wherein the term “derived” should be interpreted as meaning, in general, that the derived linear coordinates can be calculated from the angular coordinate, and also that the angular coordinate can be calculated from the derived linear coordinates, according to predetermined mathematical expressions), lying on predetermined directions. The average of the angular coordinate can be then performed based on an arithmetic average calculation performed on the derived linear coordinates. 
     According to a first aspect of the present invention, a method is provided as set forth in appended claim  1 . 
     The method comprises:
         providing at least one image frame;   processing the at least one image frame;       

     wherein the processing comprises:
         providing a description of a plurality of pixels of the at least one image frame in a color space including at least an angular coordinate descriptive of a pixel property;   calculating an average value of the angular coordinate for the plurality of pixels.       

     The method is characterized in that said calculating an average comprises, for each angular coordinate value:
         determining at least respective first and second derived linear coordinates, the at least first and second derived linear coordinates lying on at least a first and, respectively, second predetermined directions;   calculating an average value of the derived linear coordinates obtained for the plurality of pixels; and   calculating the average value of the angular coordinate based on the calculated average values of the derived linear coordinates. According to a second aspect of the present invention, a data processing apparatus as set forth in appended claim  18  is provided, adapted for performing the method of the first aspect.       

     Other aspects of the invention are set forth in the appended dependent claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The features and advantages of the present invention will be made apparent by the following detailed description of some embodiments thereof, provided merely by way of non limitative examples, in connection with the annexed drawings, wherein: 
         FIG. 1  schematically shows an exemplary scenario wherein a method according to an embodiment of the present invention for calculating average value and variance for an angular color space coordinate, particularly the H coordinate of the HSV color space, is advantageously used; 
         FIG. 2  schematically shows, in terms of functional blocks, an exemplary embodiment of a data processing apparatus that, suitably programmed, is adapted to implement the method according to an embodiment of the present invention; 
         FIG. 3  depicts, in terms of functional blocks, exemplary components adapted to implement the method according to an embodiment of the present invention; 
         FIG. 4  is a simplified flowchart illustrating the main steps of a digital video segmentation method exploiting the method according to an embodiment of the present invention; 
         FIG. 5  is an explanatory diagram of the method for calculating average value and variance for an angular color space coordinate, particularly the H coordinate of the HSV color space, according to an embodiment of the present invention; 
         FIGS. 6A and 6B  show a group of neighboring pixels and a corresponding coefficient mask for the calculation of a pixel convolution, for example to perform a high-pass filtering; 
         FIGS. 7A and 7B  show two exemplary coefficient masks for performing a Sobel high-pass filtering along the horizontal and the vertical direction on pixel luminance values, so as to determine a luminance gradient; 
         FIGS. 8A to 8D  schematically show a method of describing contours of subjects; 
         FIGS. 9A ,  9 B and  9 C schematically show a method for associating to a pixel belonging to a subject contour information about further continuation of the contour beyond the pixel itself; and 
         FIGS. 10A to 10H  are exemplary screen captures showing intermediate steps of a video segmentation process exploiting the method according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S) OF THE INVENTION 
     Making reference to the drawings, in  FIG. 1  there is schematically depicted an exemplary scenario wherein a method according to an embodiment of the present invention can be advantageously applied. In particular, the scenario considered is that of digital video segmentation: it is pointed out however that this is not to be construed as limitative to the present invention, which can be generally applied to digital image processing. 
     Two users  105   a  and  105   b , having respective data processing apparatuses  110   a  and limb (like for example PCs, notebooks, laptops, pocket PCs, PDAs, mobile or fixed videophones, set-top-boxes associated to TV screens, videoconference apparatuses, or equivalent devices) equipped with video capturing devices  115   a  and  115   b , like videocameras, and audio capturing devices, like microphones  125   a  and  125   b , are engaged in a videocommunication session. The two users are assumed to be remote from each other, where by “remote” there is intended generally physically separated, for example located in different rooms of a building, up to located in different continents of the world. The two data processing apparatuses  110   a  and  110   b  are in communication relationship through a data communications network  120 , like a LAN, a MAN, a WAN, the Internet, a PSTN/PLMN (e.g. mobile) telephony network. The connection of the data processing apparatuses  110   a  and  110   b  to the network  120 , through suitable network access points (not shown in the drawing) may be a wired connection, a wireless connection, or a mix thereof. In particular, by wireless connection there may be intended a WiFi connection, a Bluetooth connection, a GSM connection, a UMTS connection, or any other non-wired connection. 
     As mentioned above, the two users  105   a  and  105   b  are assumed to be engaged in a videocommunication session, during which they exchange both audio and video contents. In particular, at the transmitter premises (in a bidirectional communication, both the users play the role of transmitter and receiver), audio (e.g., voice) is captured by the microphones  125   a  and/or  125   b , whereas the video sequence(s) are captured by the videocameras  115   a  and/or  115   b , at the receiver premises, the captured video sequence(s) is (are) reproduced and displayed on the display device(s) of the data processing system(s), whereas the audio content is reproduced through loudspeaker/earphones  130   a  and/or  130   b.    
     It is assumed that real-time video segmentation tools are implemented at either one (or both) of the users&#39; data processing apparatuses  110   a  and  110   b . The generic video segmentation tool is adapted to analyze the videocommunication sequence captured by the cameras  115   a  and/or  115   b , so as to determine which pixels of a generic video frame of the captured video sequence belong to an image foreground subject, like for the example the user  105   a  or  105   b  (the so-called “talking head”), and which pixels belong instead to the rest of the image, forming the so-called image background. Thanks to the video segmentation tools, the users can for example decide to replace the actual background with a different one: for example, as pictorially shown in the drawing, the user  105   a , which is actually at home during the videocommunication sequence, appears to the user  105   b  on a totally different background. 
       FIG. 2  schematically shows the main functional blocks of a generic, exemplary data processing apparatus  200 , like one of the data processing apparatuses  110   a  and  110   b  of  FIG. 1 . Several functional units are connected in parallel to a data communication (e.g., a PCI) bus  205 . In particular, a Central Processing Unit (CPU)  210 , typically comprising a microprocessor (possibly, in high-performance data processing apparatuses, a plurality of cooperating microprocessors), controls the operation of the data processing apparatus  200 . A working memory  215 , typically a RAM (Random Access Memory) is directly exploited by the CPU  210  for the execution of programs and for the temporary storage of data during program execution; a Read Only Memory (ROM)  220  is used for the non-volatile storage of data, and stores for example a basic program for the bootstrap of the computer, as well as other data, like low-level configuration data for the data processing apparatus  200 . In particular, the RAM may be structured as a main RAM (typically a DRAM) and a cache RAM, typically a SRAM, and the microprocessor may embed a first-level cache RAM. The ROM may include an electrically-alterable non-volatile memory, like a Flash memory and/or an EEPROM. 
     The data processing apparatus  200  comprises several peripheral units, connected to the bus  205  by means of respective interfaces. Particularly, peripheral units that allow the interaction with a human user are provided, such as a display device  225  (for example a CRT, an LCD or a plasma monitor), a keyboard  230 , a pointing device  235  (for example a mouse), a microphone  270 , a loudspeaker and/or earphones  275 , a videocamera  280 . In particular, the display device  225  is managed by a video subsystem (also referred to as graphics accelerator)  285 , typically a PCB (Printed Circuit Board) distinct from and associated with (typically, electrically and mechanically connected to) a data processing apparatus motherboard carrying the CPU  210 . The microphone  270  and the loudspeaker/earphone  275  are similarly managed by an audio board  271 . The videocamera  280  is for example connected to a port of a Universal Serial Bus (USB) adapter  277  with one or more USB ports. In alternative, the video subsystem  285  may include a video capturing hardware, and be adapted to directly manage the videocamera  280 , particularly to directly receive captured video frames. It is pointed out that the provisions of video and/or audio boards distinct from the CPU motherboard is a common solution, but is not to be intended as limitative for the present invention, which can as well apply when for example no video and/or audio boards are provided, and the respective components as mounted on the CPU motherboard. It is also pointed out that the presence of a video subsystem is not to be intended as a limitation of the present invention: in alternative invention embodiments, the video subsystem may be absent, and the display device be managed directly by the CPU. 
     The data processing apparatus  200  also includes peripheral units for local mass-storage of programs (operating system, application programs) and data (files), such as one or more magnetic Hard-Disk Drives (HDD), globally indicated as  240 , driving magnetic hard disks, a CD-ROM/DVD drive  245 , or a CD-ROM/DVD juke-box, for reading/writing CD-ROMs/DVDs. Other peripheral units may be present, such as a floppy-disk drive for reading/writing floppy disks, a memory card reader for reading/writing memory cards, printers and the like. For the connection to the data communications network  120 , the data processing apparatus  200  is further equipped with a Network Interface Adapter (NIA) card  250 , for example an Ethernet card, a WiFi card, a Bluetooth card, or, alternatively (or in addition), the data processing apparatus  200  may be connected to the data communications network  120  by means of a MODEM, e.g. a dial-up modem, or a x-DSL modem, or a satellite modem. In the case of a smart mobile phone, a radio communications interface is provided, intended to include all the HW and SW components necessary for enabling the mobile phone access a mobile telephony network, e.g. a GSM/GPRS(EDGE) or UMTS network. 
     In a way per-se known in the art, the video subsystem  285  includes a GPU (Graphic Processing Unit, sometimes also referred to as Visual Processing unit—VPU)  287 , i.e. a programmable (co)processor devoted to autonomously perform processing of data relating to images and videos to be displayed on the display device  225 . The GPU  287  implements a number of graphics primitive operations in a way that makes running them much faster than drawing directly to the display device with the host CPU. The video subsystem  285  may also include local working memory resources  289 , for the use by the GPU; it is however noted that in last-generation PCs, featuring high-speed data buses, the video subsystems exploit the data processing apparatus working memory  215 . 
     As known in the art, modern GPUs are designed to operate as computer three-dimensional (3D) graphics generators, for the (3D) rendering process adopted for example in last-generation animation movies and videogames. GPUs are not general purpose processors as the CPUs, and even modern GPUs have a quite limited programmability; in particular, only two points of the rendering pipeline (roughly speaking, by pipeline there is intended the sequence of processing steps that, applied to input data, produces the output data) are programmable: the video board can execute so-called “vertex shader” programs and “pixel shader” programs. Generally speaking, and without entering into excessive details well known to those skilled in the art, a vertex shader program is a program that is invoked in respect of each vertex of a polygonal mesh that is used to described 3D objects to be drawn; a pixel shader program is instead a program that is invoked in respect of each pixel of the an already existing image, typically the image drawn by the vertex shader program. 
       FIG. 2  schematically depicts the internal structure of the GPU  287 ; it is pointed out that, being aspects per-se known in the art, the GPU description will not go into deep detail. The GPU  287  has a memory controller unit  290  controlling the GPU access to the local memory  289 , and including texture and geometry cache memories and a cache controller. The GPU  287  includes a plurality  291  of vertex processors, programmable for executing vertex shader programs, a plurality of pixel processors  292 , programmable for executing pixel shader programs, a plurality of texture fetch, filtering and decompression units  293  for feeding the pixel processors with filtered and decompressed textures, read from the memory  289  (and/or, possibly, from the memory  215 ), a plurality of texture and color interpolators  294 , a tile HSR (Hidden Surface Removal) logic  295 , a color and Z-coordinate (i.e., pixel depth) compression/decompression unit  296 . A frame buffer logic  297  includes anti-aliasing units (not explicitly shown), and a color and Z tile cache  298 . Video input/output interfaces  299  include for example a VGA interface for the connection of the display device  225  and/or additional interfaces, like a TV interface. 
     According to an embodiment of the present invention, the processing power of the video subsystem  285  and, in particular, of the GPU  287  is expediently exploited for performing at least part of the steps of a video segmentation algorithm, wherein said part of the steps of a video segmentation algorithm includes in particular steps for the calculation of an average value and variance for an angular color space coordinate, particularly the H coordinate of the HSV color space. This allows relieving the CPU  210  from a significant computing burden. In particular, the pixel processors  292  are expediently exploited. More particularly, according to an embodiment of the present invention, the pixel processors  292  of the GPU  287  are suitably programmed for executing one or more pixel shader programs adapted to carry out at least part of the steps of the video segmentation algorithm, as it will be described in detail in the following of the present document. It is however pointed out that the use of the video subsystem, and particularly of the GPU processing power for performing at least part of the steps of a video segmentation algorithm is not to be intended as a limitation of the present invention, which remains valid even if the video segmentation algorithm, at least as far as the calculation of the average value and variance for an angular color space coordinate, particularly the H coordinate of the HSV color space, is concerned, is performed on the CPU, not on the GPU. 
     The rules to be followed in writing a pixel shader program to be executed by the pixel processors  292  are very rigid. A pixel shader program is a program producing, as a result, the color, or the shade, to be assigned to each single pixel of an image. From a pixel shader program viewpoint, images are represented in terms of so-called “textures” (i.e. mono- or generally N-dimensional arrays), which are stored in areas of the memory  289 ; a generic image pixel corresponds to an element of a texture, wherein information regarding properties of that pixel (e.g., the color) are stored. For example, a pixel shader program may receive in input an input texture, corresponding for example to an input digital image to be processed, and generate, as a result of the processing, an output texture, corresponding to the processed digital image. If a pixel shader program is invoked in respect of a certain pixel of the image, and the output texture is assumed to be stored in an area of the memory  289 , the only location of the memory  289  that can be written is the location corresponding to the output texture element that corresponds to the considered pixel. More than one output textures can be managed by the pixel processors of the GPU, but in such a case all the output textures are to be written simultaneously. Also, it is not possible to simultaneously read from and write into memory locations wherein a same texture is stored. These restrictions stem from the fact that the GPU pixel processors process the pixels in lots, which run through the GPU hardware simultaneously, in parallel, independent processing pipelines (up to 32 independent pipelines are supported in modern GPUs), so that the result of the processing of a certain pixel does not (and cannot) affect (and/or depend on) the processing of the other pixels (e.g. of the adjacent pixels), which may be processed at the same time (or at different times) in the same or in other pipelines. For these reasons, a GPU cannot execute a program implementing a sequential algorithm, but only algorithms wherein the processing of each pixel is independent from the processing of the other pixels of the image. The Applicant has taken these constraints in due consideration in deciding which activities of the video segmentation to delegate to the GPU, and how to write the program to be executed by the GPU. 
     According to an embodiment of the present invention, the algorithm for the video segmentation method is structured in a succession of several phases, each of which involves processing, wherein the result of the processing of each pixel does not affect (and/or depend on) the remaining image pixels, and that are implemented by the GPU, plus a final phase, involving a sequential processing, which is implemented by the CPU (after a transfer of data from the GPU to the CPU). It is however pointed out that, in alternative embodiments of the invention, the number and type of phases of the video segmentation algorithm that are implemented by the GPU may vary. Possibly, no processing phases are delegated to the GPU. 
       FIG. 3  is a schematic representation in terms of functional blocks of the main components of a video segmentation algorithm according to an embodiment of the present invention; it is pointed out that the generic functional component may be a software component, a hardware component, or a mix of software and hardware. 
     The video segmentation algorithm is assumed to be implemented at either one or both the data processing apparatuses  110   a  and  110   b.    
     In particular, the functional blocks enclosed in a broken line denoted  390  correspond to operations performed by one or more pixel shader programs executed by the pixel processors  292  of the GPU  287 , whereas the functional blocks enclosed in a broken line denoted  395  correspond to operations performed by one or more programs executed by the CPU  210 . 
     An input frame  305 , e.g. a frame of the video stream captured by the videocamera  115   a  or  115   b , is fed to an RGB-to-HSV converter module  310 , for the conversion of the image description from the RGB (Red, Green, Blue) color space into the HSV (Hue, Saturation, Value) color space. 
     As known in the art, the RGB is the format used by many of the commercially available videocameras. 
     The conversion into the HSV format is preferred because the RGB color space is not perceptually uniform: in the RGB color space, numerically equal color differences in different colors are not perceived by the human eye as equal differences when the different colors are displayed; for example, if the green component is varied, the perceived color change is much more evident than in the case the blue component is varied of a same amount; differently, the metric in the HSV color space is essentially the same as that adopted by the human eye, so that working in the HSV color space produces better video segmentation results. 
     The formulas for the conversion from RGB into HSV are known in the art, and are the following: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 max = max(R,G,B); 
               
               
                   
                 mm = min(R,G,B); 
               
               
                   
                 V = max 
               
               
                   
                 S = (max − min)/max 
               
               
                   
                 if S = 0 
               
            
           
           
               
               
            
               
                   
                 then H is meaningless 
               
            
           
           
               
               
            
               
                   
                 else 
               
            
           
           
               
               
            
               
                   
                 delta = max − min 
               
            
           
           
               
               
            
               
                   
                 if R = max then H = (G − B) / delta 
               
               
                   
                 if G = max then H = 2 + (B − R) / delta 
               
               
                   
                 if B = max then H = 4 + (R − G) / delta 
               
               
                   
                 H = H*60 
               
               
                   
                 if H &lt; 0 then H = H + 360 
               
               
                   
                   
               
            
           
         
       
     
     From this conversion formulas, it can be appreciated that the S and V coordinates of the HSV space are linear coordinates, normalized to 1, and their values range from 0 to 1; the coordinate H is an angular coordinate whose value ranges from 0° to 360°. A conventional graphical representation of the HSV space is in term of a cone turned upside-down, with the apex at the bottom and the base at the top; the cone axis is the axis of the V coordinate; the axis of the S coordinate is perpendicular to the V axis; the H coordinate indicates an angle formed with the S axis by a segment lying in a plane containing the S axis and orthogonal to the V axis, and starting from the origin of the V and S axis. In such a graphical representation, the dark colors, having low luminosity, are close to the bottom of the cone (close to the apex, which corresponds to black); the primary colors (and, in general, the saturated colors) correspond to points located on the cone surface, and become brighter and brighter moving along the V axis, from the apex towards the base; the low-saturated colors, tending to gray, are located within the cone close to the V axis, whereas the points of the V axis correspond to gray tones, and white is at the top of the V axis. From this graphical representation, it is possible to appreciate that the HSV space better describes the human eye&#39;s operation: where it is more difficult for the human eye to distinguish different colors, i.e. where the luminosity is scarce, the points of the HSV space are closer to each other (i.e., the are hardly distinguishable by comparison with a threshold); on the contrary, the brighter colors, especially the saturated ones, are more clearly distinguishable, and in fact the corresponding points in the HSV space are far from each other (i.e., they are easily distinguishable by comparison with a threshold). 
     The RGB to HSV conversion is an operation that can be performed by a pixel shader program, because it can be executed on each pixel independently of the values of the other pixels of the image. Thus, the RGB-to-HSV conversion module  310  can be implemented as a (part of a) pixel shader program executed by the (pixel processors  292  of the) GPU  287 , taking the RGB values from an input texture and writing the corresponding HSV values in an output texture. However, nothing prevents that, in alternative embodiments of the invention, the RGB to HSV conversion is performed by the CPU  210 . 
     It is pointed out that even if the videocamera does not furnish the captured video stream in the RGB format, but in a different format, it is possible to obtain the RGB format by way of a conversion: for example, in the case of an undersampling videocamera, e.g. providing video frame data in the common YUV 4:2:0 format, the RGB format may be reconstructed by a suitable filtering, an operation that most of the commercially available video boards are capable of performing directly in hardware. 
     The HSV-converted video frame is fed to a background learning module  315 , adapted to build a reference image of the background, to be used in subsequent phases of the video segmentation process for deciding whether a pixel belongs to the image background or foreground. 
     In particular, in an embodiment of the present invention, it is assumed that the background remains essentially unchanged during the video sequence; this is a reasonable assumption in many applications, like for example those involving videocommunications, where the talking head is typically located in a room. However, nothing prevents that in alternative invention embodiments the background may change, so that adaptive background learning algorithms could be used. 
     The background learning module  315  is adapted to learn how the background is. For such purpose, the background learning module  315  is adapted to build a statistical model of the background, to be then used as a reference background image. In order to build the desired statistical model, a predetermined number of video frames of the sole background environment (without subjects in foreground) are captured by the videocamera and processed. The background learning module  315  calculates, for each pixel, the average of the captured video frames. Furthermore, the background learning module  315  calculates, for each pixel, the variance (or, equivalently, the standard deviation, which is the square root of the variance) of the captured video frames. It is observed that, in principle, even a single video frame might be sufficient to define a background reference image, however, due to the inherent noise of the videocamera sensor, and to possible instabilities in the scene lighting, it is preferable to consider more than one video frames; for example, 100 video frames may be regarded as a sufficiently reliable statistical sample. 
     It is also observed that building the background statistical model by calculating the average value (and the variance) of the pixel values for the prescribed number of video frames means making an assumption that each pixel may be described by a unimodal statistical distribution, i.e. a distribution wherein the different samples gather around a single (average) value; such a model is suitable in several practical cases, but for example it is not suitable in case the videocamera is not sufficiently steady, or when a flashing light is visible in background: in the latter case, two different average values, and two different variances should be calculated for the pixel values, one for the case of light turned on, the other for the case of light turned off. 
     According to an embodiment of the present invention, the background learning module  315  includes in particular average value and variance calculator modules  315   a ,  315   b  and  315   c  for the H, S and V coordinates of the color space. 
     Since the S and V coordinates of the HSV space are, as mentioned, linear coordinates, the average value and the variance for the S and V values of a generic pixel can be calculated, as known from statistics, using the following formulas: 
     
       
         
           
             
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     wherein x i  denotes the S or V value of the considered pixel for the i-th sample, and N is the total number of samples (in the present case, the predetermined number of video frames of the sole background). 
     As mentioned in the foregoing, the calculation of the average value and of the variance for the H angular coordinate cannot proceed in the same way as for the linear coordinates S and V. Just by way of example, let it be assumed that two pixels of two different video frame samples have respective H coordinates equal to 1° and 359°, i.e., in the graphical representation of the HSV space, two points very close to the S positive half-axis (due to angle periodicity, the point having H=359° corresponds to the point having H=−1°, and is thus as close to the point having H=0° as the point having H=1°): the arithmetic average would be 180°, which however is totally incorrect, because corresponds to a point located on the S negative half-axis. 
     According to an embodiment of the present invention, a method for calculating the average value (and the variance) of the H coordinate is provided that is not affected by the above-mentioned problems. 
     In detail, as depicted in  FIG. 5 , the coordinate H of a generic pixel is assumed to represent the phase (or the argument) Arg(Z) of a complex number (or, equivalently, of a vector) Z; the modulus of the complex number Z may for example be set equal to the value of the S coordinate of the pixel, or, alternatively, be put equal to 1 (this second choice may be regarded as preferable, because in such way the color information included in the value of the S coordinate, already taken into account in the calculated average and variance of the S coordinate, is kept separated from the color information included in the value of H coordinate). 
     Given the values of the phase H, the calculation of the real part  e(Z) and of the imaginary part ℑm(Z) of the complex number Z (or, equivalently, of the components of the vector Z lying on directions being orthogonal with each other) practically corresponds to calculating a sine and a cosine of the value of the H coordinate (or equivalent calculation): 
           e ( Z )=cos( H ),    m ( Z )=sin( H ). 
     The real and imaginary parts  e(Z) and ℑm(Z) of the complex number Z derived from the phase H are linear quantities, so that their average value for two or more different complex numbers Z, corresponding to the H coordinates of two or more pixels, or to a same pixel but belonging to different video frames, can be calculated as a usual arithmetic average, as described before in connection with the S and V coordinates. Denoting with  R   N  and Ī N  the average values of the real and imaginary parts  e(Z) and ℑm(Z) of a population of complex numbers Z corresponding to the H coordinate of the pixels in the various samples of the sole background, the modulus of the average of the complex numbers is: 
       |   Z     N | 2   =  R     N   2   +Ī   N   2    
     whereas the phase of their average, which is the average of the H coordinate, is: 
     
       
         
           
             
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     (where, for the purposes of the present invention, arctan is a 4-quadrant arctangent, for obtaining values of H in the range from 0° to 360°). Other mathematical formulas (e.g. arcsin, arccos, etc.) may be exploited for calculating the average of the H coordinate. 
     With regards to the variance, it can mathematically be demonstrated that, for complex numbers, the formula is: 
     
       
         
           
             
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     wherein z i  denotes the complex number corresponding to the H coordinate value of the i-th sample; in case the complex numbers z i  all have unitary modulus, it follows: 
     
       
         
           
             
               
                 
                   
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     Intuitively, if several complex numbers having unitary modulus and similar phases are averaged, the resulting, average complex number should have phase similar to the phases of the averaged complex numbers, and practically unitary modulus; if, on the contrary, several complex numbers having different phases, possibly distributed uniformly between 0° and 360°, are averaged, the resulting average complex number is a complex number having indeterminate phase (large variance of H, tending to 1) and modulus practically equal to zero. 
     It is pointed out that the calculation of the average value and the variance for the H, S and V coordinates of the pixels in the different background samples is an operation that can be carried out independently for each pixel, so that the background learning module  315  is adapted to be implemented as a (part of a) pixel shader program executed by the (pixel processors of the) GPU. 
     In particular, it is observed that up-to-date GPUs have, among the primitives of the pixel processors, the sine and cosine functions: in this case, the calculation of the real and imaginary parts of the complex number Z starting from the value of the H coordinate can be directly performed; in case the GPU does not have the sine and cosine primitives, the values for the sine and cosine functions might be tabulated and stored in memory as an array, interpreted by the GPU as a 1D texture, for example for each degree, and the desired value for the real and imaginary parts of the complex number Z can be obtained by reading the 1D texture using the value of the coordinate H as an entry. 
     In commercially available GPUs, the arctangent is instead not a primitive of the pixel processors; thus, in order to calculate the average of the H coordinate, the arctangent values may be tabulated and stored, for example, in the form of a matrix, which is interpreted by the GPU as a  2 D texture, which is then read using the average of the real and imaginary parts  R   N  and  X   N  as abscissa and ordinate, respectively. 
     It is however pointed out that, in alternative embodiments of the invention, the calculation of the average value and the variance for the H, S and V coordinates of the pixels may be an operation carried out by the CPU  210 . 
     It can be appreciated that in order to build the desired background statistical model, it is not necessary that the GPU stores all the values of all the background samples (which would probably cause a saturation of the video board memory): for calculating the summations in the above formulas it is sufficient that the GPU, as the video frames with the background samples arrive, keeps a running total of the values of the S and V coordinates and of the real and imaginary parts of the complex numbers corresponding to the H coordinate of the pixels; for calculating the variance, it is sufficient to keep a running total of the squares of the values of the S and V coordinates (whereas the variance of the H coordinate entirely depends on the modulus of the average of the complex numbers Z corresponding to the values of the H coordinate). Advantageously, since the textures used by the GPU are typically adapted to store, for each pixel, four values, corresponding to the channels R, G, B and A (alpha, i.e. an indicator of the pixel opacity), the running totals necessary for calculating the average values and the variances fit in a single texture (for example, the running totals for the real and imaginary parts  e(Z) and ℑm(Z) of the complex numbers Z can be stored in the places normally used for the R and G channels, and the running totals for the S and V coordinates can be stored into the places normally used for the B and A channels). 
     Since, as mentioned in the foregoing, the GPU cannot simultaneously read from and write into a same texture, the running totals of the S and V values, and of the squares thereof, can be calculated exploiting two textures, used alternatively in “ping-pong” mode; if, for example, for the generic, current M-th video frame the totals previously calculated are stored in the first one of the two textures, which forms the input texture for the current iteration, the pixel coordinate values of the M-th video frame are added (pixel by pixel) to those stored in and read out of the first texture, and the result of the addition is stored into the second texture; at the next, (V+1)-th video frame, the input texture is, represented by the second texture, and the values stored therein are read out and added (pixel by pixel) to the coordinate values of the (M+1)-th video frame, and the result stored in the first texture. This ping-ponging between the first and the second texture is repeated until the background learning is completed. Preferably, the textures used are in floating-point form, in order to improve precision and to avoid overflows. 
     In particular, each time a video frame is passed to the GPU  287 , the CPU also passes thereto an updated counter value providing an updated count N of the received video frames, as well as the value N/(N−1), in order to allow the GPU to calculate “on the fly” the average value and the variance, as the video frames are received and processed. 
     Coming back to  FIG. 3 , a background subtraction module  320  is adapted to generate a first, approximate foreground binary mask  380 , by comparison of a current video frame with a reference image, particularly (but not limitedly) the background statistical model built by the background learning unit  315 . In particular, the background subtraction module  320  generates the approximate foreground binary mask  380  by subtracting the background statistical model from a current video frame (converted into HSV format). For the purposes of the present description, by “mask” there is intended a matrix of elements, wherein the generic element of the matrix corresponds to an image pixel, and the value of the matrix element provides an indication of the fact that the corresponding pixel belongs to the image background or foreground. For example, a pixel belonging to the image background can be assigned a logic “0”, whereas a pixel belonging to the foreground can be assigned a logic “1”. In particular, the background subtraction module  320  receives the average values of the H, S and V coordinates, calculated by the average value and variance calculator modules  315   a ,  315   b  and  315   c  of the background learning module  315 . For every pixel of the current video frame (including the pixels belonging to the foreground subjects, e.g. the talking head to be segmented from the background), the background subtraction module  320  is adapted to compare the current values of the H, S and V coordinates to the corresponding average values  H ,  S  and  V  calculated (and stored for that pixel) by the background learning module  315 . In order to perform the comparison, a difference between the current value and the average value can be performed for the H, S, V component. If the calculated difference is relevant (for example, if it exceeds a predetermined threshold), the pixel is regarded as belonging to the foreground, and a corresponding value in the first foreground binary mask  380  is conventionally set to “1”; differently, the pixel is regarded as belonging to the background, and the corresponding value in the first binary mask  380  is conventionally set to “0” (the opposite convention may be adopted). 
     It is observed that an advantage of the adoption of the HSV description is that it allows separating the pixel color information (given by the H and S components) from that related to the pixel brightness (V component); this allows setting, for each of the three H, S and V channels, a different threshold for the recognition of the image foreground subjects. In this way, it is for example possible to remedy to the fact that, for videos captured in conditions of scarce ambient light, or in presence of light having a strong color dominance, the videocamera is typically not able to correctly evaluate the hues: by using the HSV description, the tolerance in respect of the H coordinate can be increased; if instead the light is strong and neat and enables clearly distinguishing the different colors, it is possible to increase the precision for the hue detection (H coordinate), at the same time decreasing the precision for the brightness (V coordinate), so as to reduce the effects of projected shadows (that cause a change in the luminosity of the pixels without altering their color). 
     Preferably, in order to determine that a difference between a value of one of the three H, S, V coordinates and the respective average value is significant, a comparison threshold should not be too low, otherwise the inevitable videocamera noise would cause almost all of the pixels to be erroneously regarded as belonging to the image foreground (reasonably, none of the pixels in the current video frame is identical to the its counterpart in the background statistical model, i.e. the current H, S, V values are not identical to the averages calculated in the background learning phase). Also, it is preferable not to use a fixed comparison threshold for all the pixels of the video frame, because image areas affected by noise to a different extent, e.g. due to differences in the scene luminosity, would be treated in a different way. Furthermore, the comparison threshold should preferably be adjusted each time the light conditions (and thus the videocamera thermal noise) change. The adjustment of the comparison threshold may be automatic. 
     The background subtraction module  320  is adapted to calculate, pixel by pixel, the absolute value of the difference of the value of the each of the H, S, V coordinates to the corresponding average value calculated by the background learning module  315 , and to compare the calculated absolute value difference to the standard deviation of the considered coordinate; if the calculated (absolute value) difference exceeds a value related to, e.g. proportional to the corresponding standard deviation, the difference is considered non-negligible and indicative of the fact that the pixel considered belongs to the image foreground; for example, considering the H coordinate, the difference is considered non-negligible if: 
       | H−  H |&gt;α   H √{square root over (Σ H   2 )} 
     wherein α H  is a proportionality, multiplication factor that, depending on its value, renders the operation of background subtraction more or less sensitive (in principle, the multiplication factor α H  may be any real number. By increasing the value of the multiplication factor α H , the differences necessary to consider a pixel as belonging to the foreground increase, and thus the result is less sensitive to noise; however, a high value of the multiplication factor α H  may cause the generation of a binary mask having several “holes” in the area of a foreground subject, in cases in which the color of the foreground subject resembles that of the surrounding background. The multiplication factors can be equal or different for the three coordinates H, S and V; preferably, the value of the multiplication factors may be (independently) adjusted by the user, so as to find the best trade off between robustness and precision of the foreground detection. Taking the absolute value of the differences ensures equal treatment to positive and negative differences. 
     The background subtraction module  320  is in particular adapted to combine the results of the three tests (schematically represented by a “?” in the following inequalities): 
     
       
         
           
             
               
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     performed on every pixel for the H, S and V coordinates thereof, in order to determine whether the generic pixel is a foreground or a background pixel. For example, the three test results may be combined logically in a logic AND, or in a logic OR: in the former case, all the three components (H, S, V) of a generic pixel shall differ significantly from the respective calculated average values in order for the considered pixel to be regarded as belonging to the foreground; in the latter case, it is sufficient that just one of the three components has a significant deviation for regarding the pixel as belonging to the foreground. The Applicant observes that better performances are obtained with the OR logic combination, because for the human eye it is sufficient that the hue is different for perceiving two colors as different (even if the saturation and the luminosity are the same). Other combinations of the three test results are possible; for example, in a method adapted to suppress the shadows projected by foreground subjects onto the background, the decision of whether a pixel belongs to the background, but is in shadow due to the presence of a foreground subject, may be based on the fact that the values of the coordinates H and S are almost equal to the corresponding averages, but the value of the coordinate V is decreased (compared to the calculated average) in a way similar to the decrease in the coordinate V experienced by the neighboring pixels. 
     It is pointed out that the background subtraction operation is an operation that can be carried out independently for each pixel: thus, the background subtraction module  320  is adapted to be implemented as a (part of a) pixel shader program executed by the (pixel processors of the) GPU. However, nothing prevents that the background subtraction operation is performed by the CPU  210 . 
     The logical AND and OR operations may be performed by the GPU, reproducing them by means of multiplications and/or additions of binary numbers. 
     An edge detection module  325  is adapted to detect edges in the video frames. In particular, the edge detection module  325  is adapted to detect edges in the current video frames and in the background statistical model, and to compare them. 
     Several operators are known in the art which are adapted to detect edges of subjects in digital images. Typically, high-pass filtering operators based on gradient are used. One of such operators is the so-called Sobel operator, which is a high-pass filtering operator providing good performances even in presence of relatively noisy digital images, and providing as a result essentially continuous edge lines, not fragmented into several separated segments. 
     The Sobel operator performs a convolution (practically, a weighted sum) between the digital image under consideration (in the present case, the current video frame, or the background statistical model) and a high-pass filtering mask with predetermined coefficients. The high-pass filtering mask is for example an n×n mask, e.g. a 3×3 mask, wherein the central coefficient corresponds to a pixel currently under processing within the digital image to be filtered, and the remaining coefficients apply to the neighboring pixels, as schematically shown in  FIGS. 6A and 6B ; in particular,  FIG. 6A  shows a pixel under processing (z 5 ) and its neighboring pixels in a 3×3 image portion, and  FIG. 6B  shows the high-pass filtering mask. The convolution R is calculated by centering the filtering mask on the currently processed pixel, and adding the products of the pixel values with the respective coefficients: 
     
       
         
           
             
               
                 
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     In particular, choosing a suitable high-pass filtering mask, it is possible to calculate the gradient of a certain quantity expressing a property of the pixels (like one of the coordinates H, S, V) in a predetermined direction.  FIGS. 7A and 7B  show two exemplary 3×3 masks corresponding to Sobel operators for calculating the gradient of a certain quantity along two orthogonal directions, respectively the horizontal and the vertical directions. Using the mask of  FIG. 7A , the horizontal component G x  of the gradient of a certain quantity is given by: 
         G   x =( z   3 +2 z   6   +z   9 )−( z   1 +2 z   4   +z   7 ) 
     whereas using the mask of  FIG. 7B  the vertical component G y  of the gradient is calculated as: 
         G   y =( z   7 +2 z   8   +z   9 )−( z   1 +2 z   2   +z   3 ) 
     It is pointed out that in alternative embodiments different operators may be used to detect the edges. 
     It is pointed out that the convolution operation is an operation in which the result calculated for the generic pixel depends only on the previous values of the neighboring pixels, and not on the result of the convolution calculated for the neighboring pixels; thus; it can be performed by a (part of a) pixel shader program executed by the (pixel processors of the) GPU. In particular, in a first input texture the values of the pixels of, e.g., the current video frame are stored, whereas in a second input texture the coefficients of the Sobel operator mask are stored; the GPU calculates, for each pixel, a convolution of the values of the neighboring pixels to the considered pixel based on the coefficients of the Sobel operator mask, and the result is stored into an output texture. However, in alternative embodiments, the convolution operation for determining the edges may be performed by the CPU. 
     In principle, the edges for the three H, S, and V pixel coordinates could be calculated separately, so as to obtain three distinct edge maps. The edge maps calculated for the H and S components are however not particularly useful in the determination of the image foreground subjects, partly because too noisy, partly because they add little information to the edges calculated on the basis of the image luminosity. 
     Also, it is preferable not to directly use the value of the V coordinate; since the V component is calculated as the maximum of the three R, G, B components, a peak of noise on even a single one of the three R, G, B components totally affects the value of V, thus, if in the edge detection the Sobel operator is applied to the V component, possible noise peaks would have a strong impact The edge detection is for example performed by applying the Sobel operator to the luminance value of the pixels, which is calculated as a weighted average of the R, G, B components: 
         Y= 0.299 R+ 0.587 G+ 0.114 B.    
     To this purpose, an RGB to Y converter unit  330  converts the current video frame from the RGB format into the Y format. 
     It is pointed out that the calculation of the luminance value Y is an operation carried out individually pixel by pixel; thus, it can be performed by a (part of a) pixel shader program executed by the (pixel processors of the) GPU. However, nothing prevents that the calculation of the Y value is performed by the CPU. 
     As mentioned, the edge detection module  325  is also adapted to calculated edges in the reference image, particularly in the statistical background model calculated during the background learning. To this purpose, a HSV-to-Y converter module  335  converts the average values of the H, S and V components calculated by the background learning module  315  into a corresponding Y value, for each pixel. In particular, the conversion from the HSV space into the Y value may be performed in one step or in two steps, with an intermediate conversion into the RGB space. Also the calculation of the Y value for the pixels of the statistical background model can be performed by a (part of a) pixel shader program executed by the (pixel processors of the) GPU. However, nothing prevents that the calculation of the Y value for the pixels of the background model is performed by the CPU. 
     The edge detection module  325  calculates the horizontal and vertical components G x  and G y  of the gradient of the luminance Y, and the modulus of the luminance gradient is calculated as G=√{square root over (G x   2 +G y   2 )}. The value of the gradient modulus provides an indication of whether the considered pixel belongs or not to an edge of an image subject: pixels located in essentially uniform image areas features a value of G close to zero, whereas pixels located on an edge of an image subject features high values of G. If desired, the 4 quadrant arctangent 
     
       
         
           
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     of the luminance gradient can be calculated, so as to obtain an additional indication on the angle formed by the edge with the horizontal axis. 
     The edge detection module  325  produces two edge maps  381  and  383 : the first edge map  381  is a map of the edges in the background statistical model (which can be calculated once and for all after the background statistical model has been created), and the second edge maps  383  is a map of the edges in the current video frame. The edge maps  381  and  383  include, for each pixel in the background statistical model and, respectively, in the current video frame, the respective value of the luminance gradient. 
     An edge subtraction module  340  receives the two edge maps  381  and  383 , and is adapted to compare, e.g. to subtract, the edges calculated in respect of the statistical background model with the edges calculated in respect of the current video frame. Subtracting the edges may correspond to subtracting the value of the luminance gradient calculated for the background statistical model from the value of the luminance gradient calculated in respect of the current video frame, for each pixel. 
     In particular, if the edge (luminance gradient) subtraction gives a positive value for a given pixel, then the pixel under consideration is regarded as belonging to an edge of the current image that was not present in the background statistical model: such a pixel thus reasonably belongs to a foreground subject. A negative value is instead an indication of the fact that the considered pixel belongs to an edge present in the background, but not in the current image: this may reasonably mean that the edge in the background is hidden (or occluded) by the foreground subject, e.g. by the talking head, so there is a good probability that the considered pixel also belong to the foreground. A luminance gradient difference value close to zero is an indication that the pixel belongs to a relatively uniform area, or that it belongs to an edge that was present in the background and that remains unaltered in the current video frame; in this case, no useful information is provided. 
     The edge subtraction module  340  generates a pixel by pixel map  385  of the edge differences, pixel by pixel. The map of edge differences  385  and the approximated foreground binary mask  380  generated by the background subtraction module  320  can be fed to a foreground mask completion module  345 , adapted to combine, pixel by pixel, the information included in the first approximated foreground binary mask  380  with the information included in the map of edge differences  385 . 
     In particular, the first approximated foreground binary mask  380  is stored in a first GPU input texture, the map of edge differences  385  is stored in a second GPU input texture, and the foreground mask completion module  345  is adapted to load the first and the second input textures; the foreground mask completion module  345  is then adapted to transform the edge difference values contained in the map of edge differences  385  into Boolean values, by comparison of the absolute difference values with a predetermined threshold, so to obtain an intermediate edge differences binary mask. For example, a suitable threshold may be 0.5: if the difference between the edges (i.e., between the luminance gradients) in the current video frame and those in the background statistical model exceeds the value of 0.5, then it is likely that the pixel belongs to the foreground, and that pixel, in the edge differences binary mask, is set to “1”. Then, the binary values in the approximated foreground binary mask  380  are combined in logic OR with the Boolean values in the edge differences binary mask  385 . It is pointed out that similar or equivalent operations may be performed by the CPU without involving the GPU. 
     By combining the approximated foreground binary mask with the map of the edge differences (transformed in binary form), the foreground mask completion module  345  is adapted to complete (or at least to add information to) the approximated foreground binary mask  380 , which as mentioned is a first, rough approximation of the foreground mask, by adding thereto those pixels which are characterized by a “1” in the edge differences binary mask; the added pixels&#39; typically lie at the borders of the foreground area, and are particularly important because they are located in image areas wherein the background and the foreground colors are blended, so that the background subtraction may provide erroneous results. Furthermore, it is observed that at the borders of the foreground subjects the attention of a viewer is typically attracted, thereby even small adjustments and corrections are very important for the quality of the perceived result of the segmentation algorithm. 
     It is pointed out that the comparison threshold used in the process of assigning to an edge difference value a Boolean value may be adjustable: for example, the user may prefer a segmented foreground subject with sharp rather than smooth contours: in the first case, more pixels belonging to the foreground subject contours are added to the approximated foreground binary mask, whereas in the second case less pixels are to be added. The adjustment may be automatic, based on measures performed on the videocamera: if even a slight (a displacement of even 1 or 2 pixels) videocamera movement is detected, the majority of the contours will change in position, so the comparison threshold should be increased; if on the contrary the videocamera is steady and the scene is well illuminated, the perceived contours do not change, and the comparison threshold may be decreased. 
     The high-pass filtering, the calculation of the map of edge differences  385  and the mask completion are operations that can be performed by the GPU; however, nothing prevents that, in alternative embodiments, one or more of these operations may be performed by the CPU. 
     It is pointed out that, in alternative embodiments, the foreground mask completion module  345  may use, for completing the approximated foreground binary mask  380 , the edge map  381 , or the edge map  383 , instead of their difference, or the mask completion operation may be dispensed for. 
     The (completed) foreground binary mask  387  is then preferably fed to a low-pass filtering module  350 , adapted to perform a low-pass filtering, particularly albeit not limitedly a Gaussian filtering, directed to reduce (or even eliminate) singularities, i.e. pixels of value (“0” or “1”) different from all the surrounding pixels, and thus to improve the spatial correlation of the foreground mask. In this way, isolated noise peaks in the background area, that may have caused, in the completed foreground binary mask  387 , isolated pixels or small clusters of pixels classified as foreground (i.e. conventionally characterized by a“1”) in the completed foreground binary mask  387  may be removed, being them erroneous (in the final, segmented image, these pixels would be visible as a sort of “snow” superimposed on the image); similarly, small-sized “holes” in the foreground area, i.e. pixels classified as background (i.e. characterized by a “0”) and surrounded by a large number of pixels classified as background, typically caused by random similarities between the foreground color and the background color (due for example to light reflection, chromatic particulars of the surfaces, or noise) can be removed. 
     The low-pass, particularly Gaussian, filtering is performed in a way similar to that described for the Sobel operation: a low-pass filtering mask is applied in succession to the pixel values in the completed foreground binary mask  387 . The number and values of coefficients in the low-pass filtering mask depend on the needed strength of the filtering action, i.e. on the spatial correlation among the pixels: the higher the spatial correlation, the wider the filtering mask. 
     For example, assuming that a videocamera provides source video frames in the CIF format (352×288 pixel), a 9×9 low-pass Gaussian filtering mask is suitable. 
     A known mathematical property of the Gaussian filter is its separability: thanks to this property, instead of performing the convolution in a single step, which, for a 9×9 filtering mask, would mean processing, for each pixel, 81 pixel values (so that the GPU should perform, for each pixel of the completed foreground binary mask  387 , 81 multiplications and additions, and 81 texture fetches), an identical result can be obtained by performing the convolution in two steps, involving a horizontal and a vertical pixel scan; for each pixel, a number of 9×1 and 1×9 pixel values are to be processed in each scan, for a total of 18 operations for each pixel. The coefficients of the low-pass filtering masks used in the horizontal and vertical scans are for example the following:
         [0.01171875; 0.046875; 0.11328125; 0.1953125; 0.265625; 0.1953125; 0.11328125; 0.046875; 0.01171875]       

     It can be appreciated that the filtering mask coefficients are not integer: thus, after the convolution the result is no more a binary mask, rather a mask of real numbers ranging from 0 to 1, and the generic mask element may be interpreted as representing the probability that the corresponding pixel belongs to a foreground subject. In order to re-obtain a binary mask, the generic real number may be compared to a predetermined threshold, e.g. 0.5, so as to re-obtain a binary value (depending on the comparison result: lower or higher than 0.5) which, compared to the corresponding value in the completed foreground binary mask, provides a more reliable indication that a pixel belongs to the foreground or the background. It is pointed out that the comparison threshold may be put closer to one of the two extremes of the [0;1] interval of values, so as to bias the decision in a sense or in the opposite. For example, if an error made by considering a background pixel as belonging to the foreground is considered less dangerous, for the final result, with respect to the opposite, the comparison threshold may be decreased. 
     It is pointed out that as regards the type of low-pass filtering, the size of the filtering mask, the values of the filtering mask coefficients, other choices can be made by the skilled in the art. Also, while the operation of low-pass filtering can expediently be performed by the GPU, nothing prevents that, in alternative embodiments, all or part of this operation is performed by the CPU. 
     The (filtered completed) foreground binary mask  388  is preferably fed to a morphological closing module  355 , adapted to perform an operation of morphological closing of the foreground image defined by the binary mask. 
     As known in the art, the morphological closing is an operation adapted to correct at least some of the artifacts present in the foreground binary mask, particularly artifacts in the form of holes in the foreground subjects, caused for example by similarities between the color of the foreground subject and of the underlying background pixels. 
     In particular, three types of artificial “holes” may be present in the foreground binary mask:
         very small holes (of the diameter of few pixels), caused by chromatic details of the foreground subject that do not significantly differ from the background (like the color of the hairs, or of patterns in the foreground subject clothes, or of accessories like wristwatches, glasses, and the like; for example, a tie with dots of the same color as the background may lead to several small, isolated undesired holes in the foreground binary masks);   large holes, when there are large areas of the foreground subject that do not significantly differ in color from the background (for example, a talking head wearing a red shirt with a red wall on the background);   actual holes (not to be suppressed), caused for example by the particular shape of the foreground subject, or by the particular position of the foreground subject (for example, when the talking head places his/her hands on his/her sides, the background area visible between the arm and the trunk is not to be considered part of the foreground subject).       

     The morphological closing operation is particularly adapted to eliminate the first type of artifacts. 
     In particular, the morphological closing operation is carried out in two steps. In a first step (also referred to as a “mask dilation”), the foreground subject areas in the filtered, completed foreground binary mask  388  are expanded, or “dilated”; then, in the second step (also referred to as a “mask erosion”), the foreground subject areas in the mask are brought back to their original dimensions. The elimination of the artifacts is achieved thanks to the fact that, after the mask dilation, small holes possibly present in the foreground subject areas are absorbed by the foreground, and, after the erosion operation, they disappear. 
     In greater detail, all the pixels in the (filtered, completed) foreground binary mask  388  are processed; for each pixel, a certain number of neighboring pixels are considered, for example all those pixels contained in a rectangle (the “dilation window” or “dilation mask”) of predetermined size, like 3×3 pixels or 9×9 pixels (preferably, the size of the dilation mask depends on, in particular it is equal to the size of the low-pass filtering mask used in the low-pass filtering module  350 ). In the dilation step, the value (“1” or “0”) characterizing the pixel under processing in the foreground binary mask is replaced by the maximum among the value of the considered pixel and the values of the neighboring pixels; thus, the value of a generic pixel initially equal to “0” (i.e., a background pixel), is changed from “0” to “1” if even a single one of the (e.g., 8 or 80) neighboring pixels is a “1” (in case the opposite convention is adopted for indicating foreground pixels, the minimum is taken instead of the maximum). 
     As in the case of the Gaussian filtering, both the dilation and the erosion operations are separable into two elementary operations, performed along the horizontal and the vertical directions. 
     After the dilation, the obtained dilated foreground binary mask is rather compact and regular, in terms of distribution of the “1”s, even if it was initially irregular and with several holes in the foreground subject area. However, the dilation operation causes the contours of the foreground subject to be expanded, and isolated pixels or small pixel clusters, that remain in the background area after the low-pass filtering operation, are enlarged by the dilation operation. 
     In the subsequent mask erosion phase, the value of the generic pixel is replaced by the minimum (maximum, if the opposite convention is adopted for indicating foreground pixels) among its own value and the values of the neighboring pixels. After the erosion operation, the size of the foreground subject in the foreground binary mask returns to the initial size, still preserving the properties of compactness and regularity achieved after the dilation phase. Isolated points (single pixels or small pixel clusters) in the background areas return to their original size; however, if such points are located within or close to the foreground subject, they tend to be absorbed into the foreground subject. 
     Let a generic pixel s 0  be considered; under the assumption that the dilation operation is performed in two phases, along the horizontal and the vertical directions, let the horizontal scanning be considered; for the pixel s 0 , the neighboring pixels (using a 9×9 mask) are:
         s −4 |s −3 |s −2 |s −1 |s 0 |s 1 |s 2 |s 3 |s 4          

     As discussed above, under the assumption that a foreground pixel is indicated by a “1” in the foreground binary mask, the dilation phase provides for replacing the value (in the filtered, completed foreground binary mask  388 ) of the pixel s 0  with the maximum among the values of the pixel s 0  and the eight neighboring pixels. In the subsequent erosion phase, the value of the pixel s 0  is replaced by the maximum among the values of the pixel s 0  and the eight neighboring pixels. 
     The Applicant has observed that the conventional way of performing the morphological closing operation does not always provide good results. In particular, foreground subject edges that were already well defined in the filtered, completed foreground binary mask  388  may be altered after the morphological closing operation, so as to cause artifacts in the foreground subject. 
     For example, relatively small spaces between the talking head&#39;s fingers, or the areas surrounding the hair and the neck, or the armpits, that for a correct segmentation have to be considered as belonging to the image background, could not be preserved by the morphological closing: in particular, real holes in the foreground subject, relatively small in size and close to the foreground subject&#39;s contour, may disappear. 
     In order to solve this problem, the Applicant has devised a new, improved method for performing the dilation and erosion operations. 
     In the dilation phase the maximum is calculated starting from the pixel under processing (which is located in the middle of the dilation/erosion mask) and proceeding towards the periphery of the dilation/erosion mask: as soon as a pixel is encountered that belongs to an edge of the foreground subject, the proceeding towards the periphery is stopped, and the maximum is calculated using less pixels than those present in the dilation mask. 
     In order to perform the modified morphological closing, the morphological closing module  355  receives, in addition to the (filtered, completed) foreground binary mask  388 , the map  385  resulting from the edge subtraction provided by the edge subtraction module  340 . In order to assess whether a pixel belongs to an edge of the foreground subject, the morphological closing module  355  may be adapted to identify, in the map  385  of edge differences, positive edge differences that exceed a predetermined threshold (so as to have a tolerance against noise). 
     For example, let it be assumed that the pixels s −2  and s 3  belong to a foreground subject edge: in the dilation phase the value of the pixel s 0  is replaced by the value max(s −1 ,s 0 ,s 1 ,s 2 ), without considering the remaining pixels included in the dilation mask, which are “beyond” the foreground subject edges compared to the pixel under processing. The pixels, like s −2  and s 3  in the considered example, belonging to the edges may or may not be considered in the calculation of the maximum; the Applicant has observed that better results can be obtained by not including the edge pixels in the calculation of the maximum. In this case, assuming, e.g., that the pixels s −1  and s 1  both belong to edges of the foreground subject, the value of the pixel s 0  is left unaltered. If none of the neighboring pixels included in the dilation/erosion mask belongs to an edge of the foreground subject, the dilation operation coincides with the replacement of the value of the pixel under processing with the maximum among the value of the considered pixel and those of all the neighboring pixels defined by the chosen dilation mask. 
     A similar operation is performed in both the dilation and the erosion phases, and in particular in both the horizontal and the vertical scans of the dilation and the erosion operations. It is worth noting that, in the erosion phase, the order opposite to that followed in the dilation phase should be respected (i.e., for example, horizontal scan first, followed by the vertical scan in the dilation phase and then vertical scan first, followed by the horizontal scan in the erosion phase), in order to avoid “bypassing” of the edges. 
     In other words, the morphological closing operation is “guided”, that is, controlled, by the edges of the foreground subject. 
     The result of the edge-guided morphological closing is a closed foreground binary mask  389  wherein small holes and irregularities previously present in the (filtered, completed) foreground binary mask  388  have been filled. 
     Compared to a morphological closing operation not guided by the foreground subject edges, i.e. performed by taking, for the generic pixel being processed, the maximum and the minimum of the values of all the neighboring pixels specified by the dilation/erosion mask, the edge-guided morphological closing operation avoids altering the foreground subject contours that were already well defined in the (filtered, completed) foreground binary mask  388 , like, for example, relatively small spaces between the talking head&#39;s fingers, or the areas surrounding the hairs and the neck, or the armholes, that for a correct segmentation have to be considered as belonging to the image background, are preserved. In other words, the edge-guided morphological closing operation allows eliminating or significantly reducing artifacts of the first type mentioned above, preserving the real holes close to the foreground subject contour, even if relatively small in size. 
     In alternative embodiments, instead of using the map of edge differences  385 , the edge-guided morphological closing module may exploit either one or both of the edge maps  381  and  383 . 
     It can be appreciated that the dilation and erosion operations are adapted to performed by the pixel processors of the GPU, because both in the dilation and in the erosion phase, the values taken as inputs are the original ones, not those modified by the dilation and respectively erosion operations being executed. Thus, the dilation and erosion operations can be implemented as a (part of a) pixel shader program, executed by the (pixel processors of the) GPU. In particular, if, as for the Gaussian filtering, the dilation and the erosion operations can be separated into two elementary operations, performed along the horizontal and the vertical directions, so that the number of texture fetches required for the generic pixel can be reduced, for example from 2×81=162 to 2×2×9=36 (in the exemplary case of a 9×9 dilation/erosion mask), with a reduction of the order of 78% of the calculation complexity. 
     It is pointed out that, however, nothing prevents that, in alternative embodiments, the dilation and erosion operations (either edge-guided or not) are performed by the CPU. 
     The morphologically closed foreground binary mask is useful for performing the segmentation of the foreground subject(s) in the current video frame. 
     In order to further reduce the possible artifacts in the reconstructed video sequence, in addition to the information contained in the foreground binary mask, information about the contours of the foreground subject(s) are also provided. 
     In particular, after the (optional) edge-guided morphological closing operation, the foreground binary mask  389  is fed to an image contour extraction module  360 . The map of edge differences  385  produced by the edge subtraction module  340  may also be provided to the image contour extraction module  360 . The image contour extraction module  360  is adapted to assess whether the generic pixel of the current video frame belongs to a contour of the foreground subject. It is pointed out that performing the contour extraction on the morphologically closed foreground binary mask is just a possibility: in alternative embodiments, the contour extraction may be accomplished on the low-pass filtered foreground binary mask, or on the completed foreground binary mask, or even on the first, approximated foreground binary mask. 
     Conventionally, in digital image processing, an subject contour is considered as formed by pixels. 
     In particular, and merely by way of example,  FIG. 8A  shows a portion of a video frame wherein a foreground subject having the shape of a triangle is present; the pixels of the foreground triangle are marked as “X”, whereas the background pixels are marked as dots “•”. In the example considered, the pixels marked as “C” in  FIG. 8B  are conventionally considered to form the contour of the triangle. 
     The Applicant has observed that it may become very difficult to identify the contours of the image subjects by following the pixels identified as contour pixels: an extremely high number of ambiguous situations that are not easily solved can be encountered. For example, let the pixel labeled  805  in  FIG. 8A  or  8 B be considered, belonging to the contour of the triangle. The considered pixel  805  has adjacent thereto other four pixels identified as contour pixels. Let it be assumed that it is desired to follow the contour of the triangle clockwise: once arrived at the pixel  805 , it is not possible (unless based on a high-level observation of the whole triangle) to determine which will be the next pixel in the contour scanning: as far as only a local analysis of the surroundings of each pixel is performed, either the pixel  807   a , or the pixel  807   b , or the pixel  807   c  could be the next contour pixel; the ambiguity can be resolved if, instead of a local pixel analysis, the whole subject shape is considered: this would allow determining that the next contour pixel is the pixel  807   a . In other words, in order to follow the contour of an image subject, a local analysis of the surroundings of the generic pixel is normally not sufficient, and it is instead necessary to have an overall knowledge of the subject, which, in practical cases, is very computation-intensive and sometimes even impractical. 
     In order to overcome the problems evidenced above, the concept of border between two generic adjacent pixels has been introduced by the Applicant, so that an image contour is not considered formed by pixels, but is formed by borders between adjacent pixels. 
     For example, referring to  FIG. 8C , the triangle contour, instead of being considered formed by the pixels marked as “C” like in  FIG. 8B , is considered formed by the (20) horizontal and vertical line segments around the pixels marked as “X”. Considering again the pixel  805 , it can be appreciated that with this description, even by means of a local analysis, there are no ambiguities about how to follow the triangle contour; for example, if it is desired to follow the triangle contour clockwise, the pixel borders can be followed applying the conventional criterion that the background (i.e., pixels marked as “•”) has to be kept on the left: thus, the upper horizontal border  810  is followed from left to right, then the right vertical border  815  is followed from top to bottom, thus arriving at the upper horizontal border of the next pixel  820 , and so on, until the starting pixel border is reached. 
     Each pixel has four borders around it. Each pixel is for example assigned the ownership of two of the four borders between the considered pixel and the pixels adjacent thereto, for example the upper border and the left-hand border. As schematically depicted in  FIG. 8D , pixel a owns the borders a l  and a u  (shared with adjacent pixels d and e), whereas the border between pixel a and pixel c is the upper border c u  owned by pixel c, and the right-hand border between pixel a and pixel b is the left-hand border b l  owned by pixel b. 
     A GPU texture can be used to store, for each pixel, the following data:
         whether either one or both of the (left-hand and upper, in the present example) borders owned by that pixel is or is not part of a contour of a foreground subject. This can be determined by a local analysis of the pixel surroundings, i.e. by examining the adjacent pixels located on the left and above with respect to the considered pixel: if the considered pixel and the adjacent pixel are of the same type (both background or both foreground pixels), the associated pixel border is not part of a contour of a foreground subject; if, instead, the upper adjacent pixel and/or the left-hand adjacent pixel are of different type compared to the considered pixel (i.e. one belonging to the background, the other to the foreground), the associated pixel border is part of a foreground subject contour. For example, considering the pixel labeled  825  in  FIG. 8C , both of the borders that pixel  825  owns are part of the triangle contour, because pixels  830  and  835  belong to the background, whereas pixel  825  belongs to the foreground; considering instead pixel  840 , only the left-hand border is part of the triangle contour, because the left-hand adjacent pixel  850  is part of the background, whereas the upper adjacent pixel  845  is part of the foreground like pixel  840 ;   in case one or both of the borders owned by the considered pixel are part of a foreground subject contour, the direction to be followed when traveling on the pixel border; for example, assuming by convention that an image contour has to be followed clockwise, if the pixel belongs to the foreground the left-hand border is to traveled upwards, whereas the upper border is to be traveled from the left to the right, and vice versa for a pixel belonging to the background.       

     As an alternative to storing the direction (or, possibly, in addition thereto), if a border of a pixel belongs to a foreground subject contour, it is possible to determine, and store in the texture, in association with that pixel, information adapted to describe where (i.e. at which pixel border) the contour continues. For example, let the situation of  FIG. 9A  be considered, wherein A, B, C, D, F, G, H and I are the pixels adjacent to a generic pixel E; let it be assumed that the upper border of the pixel E belongs to a contour of a foreground subject (as represented by a “-” over the pixel E in  FIG. 9A ). From there, the contour might continue to either one of the positions denoted  1 ,  2 ,  3 ,  4 ,  5  or  6  in  FIG. 9A . Similarly, referring to  FIG. 9B , let it be assumed that the left-hand border of the pixel E belongs to a contour (as represented by a “-” on the left of the pixel E in  FIG. 9B ): from there, the contour may continue to either one of the positions denoted  1 ,  2 ,  3 ,  4 ,  5  or  6  in  FIG. 9B . It can be appreciated that the belonging of the pixel labeled I in the drawings to either the foreground or the background (i.e., the value, “1” or “0”, of that pixel in the foreground binary mask  389 ) is not influent in the determination of where the contour continues after pixel E. Thus, for each generic pixel, only eight pixels need to be considered for determining where a possible contour continues; since each of the eight pixels may take a value equal to “1” or “0”, a total of 256 possible combinations exist. Thus, a pixel shader program may be designed so that, using a look-up 1D texture with 256 positions, schematically represented in  FIG. 9C  and denoted as  910 , the pixel shader program generates, for each pixel, two values, each one ranging from 0 to 6, adapted to establishing whether, for each of the two borders owned by that pixel, the border belongs to a foreground subject contour, and, in the affirmative case, where the contour continues. In particular, the value 0 may be reserved for identifying a pixel border than does not belong to a contour. For example, considering again the pixel  825  in  FIG. 9C , the respective value corresponding to the particular arrangement of background and foreground pixels is {0000101} (it has to be reminded that the lower right-end foreground pixel of the square in  FIG. 9C  is not considered), which univocally corresponds to a location  915  in the 1D texture  910 , wherein the pair of values (3;3) is stored: the first value is associated with the left-hand pixel border, whereas the second value is associated to the upper border (see  FIGS. 9A and 9B ). In other words, the value corresponding to the binary-coded number representing the arrangement of foreground/background pixels in the positions A, B, C, D, E, F, G, H of a generic image area around the generic current pixel in position E is used as an accession key for accessing the 1D look-up texture  910 . The pair of values defining, for the generic pixel, whether the left-hand and upper borders are part of a foreground subject contour, and, in the affirmative case, where the contour continues is stored in an output texture  393 . 
     For pixels located at the edges of the video frame, the pixels missing in the pattern of neighboring pixels are, by default, considered as background pixels. The output texture  393  may be generated so as to correspond to an enlarged video frame, including an additional pixel column at the right-hand video frame edge and an additional pixel row at the bottom video frame edge (both the additional row and the additional column including background pixels), in order to enable considering of the right-hand border of the pixels belonging to the last right-hand video frame column, as well as the bottom border of the pixels belonging to the bottom video frame row. 
     Coming back to  FIG. 3 , the contour extraction module  360  is preferably adapted to exploit the information included in the edge difference map  385  to verify whether the current pixel (or pixels neighboring thereto) belongs to an edge of the foreground; for example, information may be derived related to whether, moving far from the considered pixel (for relatively few pixels) towards the inner part of the foreground subject (i.e., moving far from the background) high values in the edge difference map  385  (high absolute difference values, or high positive difference values) are encountered. Such indication is preferably stored, in the output texture  393 , in respect of each pixel, to be used in a scene analysis phase performed by the scene analysis module  365 . 
     The output texture  393  of the contour extraction module  360  is then transferred from the GPU to the CPU, for the final operations, which are to be implemented in sequential form and thus are not adapted to be executed by the pixel processors of the GPU. It is pointed out that albeit having the GPU perform the contour extraction operation is advantageous, nothing prevents that, in alternative embodiments, this operation is performed by the CPU. 
     In particular, a scene analysis module  365  is adapted to use the results of the contour extraction module  360  to follow the contours of the foreground subjects, so as to determine and store ordered lists of pixels that belong to the foreground subject contours. The scene analysis module  365  may also establish hierarchical relationships between the determined contours, i.e. between different areas of the current video frame. 
     In particular, once the pixels of a contour of a foreground subject have been identified and put in an ordered list, the area of the image enclosed within the contour is completely determined. On this area, it is possible to perform high-level processing such as for example calculating the surface area, or the “bounding box” thereof, i.e. a circumscribed square or rectangle, so as to assess whether the image zone is sufficiently wide to be taken into consideration or rather it can be neglected. Alternatively or in combination, holes in the determined foreground subject areas that contain further foreground subject areas thereinside could be filled. Alternatively or in combination, isolated foreground areas that do not touch the video frame edges could be discarded (a talking head usually does not have separated parts, and at least touches the bottom edge of the video frame). 
     The closed foreground binary mask  389  resulting from the operation of edge-guided morphological closing and the ordered list(s) of pixels forming the contour(s) of the foreground subject(s) are fed to an encoder module  370 , e.g. complying with the MPEG standard, together with the current video frame  305 . The encoder module  370  implements a foreground mask correction adapted to correct the closed foreground binary mask  389  taking into account the foreground subject contours conveyed by the ordered list(s) of pixels forming the contour(s) of the foreground subject(s) provided by the scene analysis module  365 . The encoder module  370  generates an MPEG transport stream  397  that corresponds to the segmented foreground for the current video frame  305 , and which is fed to a transmitter module  375  for transmission to a remote receiver, via the NIA/MODEM  250 . 
     A video segmentation method exploiting the angular pixel coordinate averaging method according to an embodiment of the present invention will be now described, with the aid of the simplified flowchart of  FIG. 4 . 
     The video frames making up the video stream captured by the videocamera are fed to the GPU (block  405 ). 
     Preliminarily, at the beginning of a video sequence, a background learning phase is provided, wherein a statistical model of the background is obtained, as described in detail in the foregoing. The talking head is requested to leave the scene for a while, and a sufficiently high number (e.g. 100) of video frames are captured; each video frame, for example originally in RGB format, is converted into HSV format (block  415 ), and then the average value and the variance for each of the three coordinates H, S and V are calculated (block  420 ); in particular, as discussed in the foregoing, for the calculation of the average value and variance of the H (angular) coordinate, the averaging method described in the foregoing is adopted. These operations are repeated, i.e. the background learning phase lasts until the prescribed number of samples of background have been acquired (decision block  425 ). 
       FIG. 10A  is a screen capture showing an example of a background statistical model obtained after the background learning phase (the screen captures presented are in black and white merely for compliance to patent documentation rules, albeit originally they were in color). 
     The talking head can now enter the scene. 
     The video frames making up the video stream captured by the videocamera are repeatedly fed to the GPU (block  405 ), and converted from the RGB space into the HSV space (block  430 ).  FIG. 10B  is a screen capture of an exemplary current video frame. 
     The approximated foreground binary mask  380  is built by means of the background subtraction process described in the foregoing, which involves comparing, for each pixel, the values of the coordinates H, S, V in the current video frame to the average values calculated in the background learning phase, and assigning to the generic pixel the value “1” or “0” based on the said comparison (block  435 ). In particular, as described in the foregoing, the absolute values of the differences of the coordinates H, S and V in the current video frame to the corresponding average values are calculated, and compared to the standard deviation of the respective coordinate (or to a value proportional thereto), and the results of the tests on the three coordinates are combined e.g. OR-ed or AND-ed together, so as to determine whether the value to be assigned to a pixel is a “1” (pixel presumably belonging to the foreground) or a “0” (pixel presumably belonging to the background).  FIG. 10C  is a screen capture of a segmentation that would result by using the approximated foreground binary mask  380 : several “holes” in the area of the talking head are visible. 
     Then, the approximate foreground binary mask is completed. 
     To this purpose, for each pixel of the current video frame, and for each pixel of the background statistical model, the luminance value is then calculated (block  440 ). 
     Edges in the current video frame and in the statistical model of the background are then detected (block  445 ), applying a high-pass filtering, e.g. a Sobel operator, to the pixel luminance, as described in the foregoing.  FIG. 10D  is a screen capture showing the map of the edges for the current video frame. 
     The edges in the background statistical model are then subtracted from the edges in the current video frame ( 450 ), and the map  385  of edge differences is built, as described in the foregoing; the previously built approximated foreground binary mask is completed (block  455 ) exploiting the information included in the map of edge differences, to obtain the completed foreground binary mask  387 .  FIG. 10E  is a screen capture of the completed foreground binary mask: it can be appreciated that the foreground subject contours are better defined, and this will cause less artifacts in the reconstructed, displayed video sequence. 
     The completed foreground binary mask  387  is then submitted to a low-pass (e.g., Gaussian) filtering (block  460 ), to obtain the filtered completed foreground binary mask  388  ( FIG. 10F  is a screen capture of the mask of  FIG. 10E  after filtering), and then the edge-guided morphological closing of the mask is performed (block  465 ), exploiting the information included in the map of edge differences for determining which pixels belong to an edge of the foreground subject.  FIG. 10G  shows the mask of  FIG. 10F  after the edge-guided morphological closing. 
     There follows the contour extraction operation (block  470 ), adapted to determine and store, for each pixel, information regarding the fact that either one or both of the two borders owned by the pixel belong to a contour, and, in the affirmative, where the contour continues. This operation completes the sequence of operations performed by the pixel processors of the GPU. 
     The data are then passed to the CPU, which performs the analysis of the scene, based on the information received from the GPU, in order to determine and store ordered lists of pixels belonging to the contours of the foreground subjects. 
     The procedure is repeated on the next and following frames of the video sequence (decision block  480 ). 
     The procedure for the recognition of the different image zones and the storage of the respective contours in the scene analysis module  365  (see  FIG. 3 ) encompasses one scan only of the current video frame, in raster order, and is therefore relatively fast and cache-friendly. A possible embodiment of the algorithm is described hereinbelow. 
     Considering the generic current video frame, the output texture  393  is raster scanned line by line, starting for example from the uppermost line, leftmost pixel. 
     For each line of the current video frame, and for each pixel of the considered line, it is ascertained whether the left border of the pixel under consideration belongs to a not previously encountered contour. 
     If encountered, a contour is followed until it forms a closed loop, i.e. until it returns to the first encountered pixel of the contour, and all the pixels belonging thereto are properly marked. 
     In particular, for each pixel, two contour identifiers are defined, IDleft and IDup, respectively for the contour to which the left-hand border of the pixel belongs, and for the contour to which the upper border of the pixel belongs. 
     At the beginning of the video frame raster scan, the two contour identifiers IDleft and IDup are set to 0 for all the pixels; a 0 indicates that the contour has not yet been explored (or that the pixel borders do not belong to any contour). 
     Also, a variable last-contour is exploited, whose value is used to define a contour identifier; such a variable is initially set to 0, and it is increased by one each time a new contour is encountered. 
     A further border-type variable is exploited, whose values are used to define whether the pixel border belonging to the contour is a left-hand border or an upper border. 
     Another variable contour-length may also be exploited, whose value defines the length (i.e. the number of pixel borders) of the contour. 
     During the raster scan, for the generic pixel under consideration it is ascertained whether the left-hand border thereof belongs to a foreground subject contour, which means having, in the output texture  393 , a value different from 0 as the first of the pair of values associated to that pixel. 
     When such a pixel is encountered, it is ascertained whether, for that pixel, it is IDleft=0: in the affirmative case, a new contour has been encountered: the value of the variable last-contour is increased by one, the value of the border-type variable is set to left, and the value of the variable contour-length is set to 0. The following operations are then repeated until the whole contour has been followed:
         for the pixel considered, the one between the identifiers IDleft and IDup that corresponds to the value of the variable border-type is set equal to the value of the variable last-contour;   using the information contained in the output texture  393  it is ascertained whether the contour continues with a left-hand pixel border or with an upper pixel border; for example, referring to  FIG. 9C , when the pixel  825  is encountered, from the value (3;3) stored in respect thereof it is possible to ascertain that the contour to which the pixel left-hand border belongs continues with the upper border of the same pixel;   the values stored in the output texture  393  in respect of that pixel are used to determine the image line and/or column increments to be applied to move to the next pixel where the contour continues;   the variable contour-length is incremented by one.       

     For example, considering again the pixel  825  of  FIG. 9C , after encountering the contour at the left-hand border thereof, the next iteration concerns again the pixel  825  (the line and column increments are 0 in such a case), and particularly the upper border thereof; the identifiers IDup for the pixel  825  is set equal to the value of the variable last-contour, so as to declare that the upper border of the pixel  825  belongs to the same contour as the left-hand border thereof, the position of the next pixel is determined (namely, the pixel  805 , in  FIG. 8A ), the variable contour-length is incremented by 1, and so on. 
     These operations are repeated until the pixel coordinates (line and column) coincide with the saved coordinates of the first pixel encountered for that contour. 
     When instead, during the raster scan, a pixel is encountered whose left-hand border thereof belongs to a foreground subject contour, but the value of the identifier IDleft is different from 0, a value of a variable inside, associated with the contour identified by the value of the identifier IDleft of the pixel and initially set to false when the contour is identified the first time, is set to negation of its previous value, so as to denote that the pixel under processing is inside the contour identified by the value of the identifier IDleft of the pixel under processing. 
       FIG. 10H  shows the result of the segmentation process: the original background has been replaced with a different one, in this case a monochrome background. The result is relatively neat and without evident artefacts. 
     The present invention can be implemented rather easily, for example by means of suitable software. However, implementation in software is not to be intended as a limitation to the present invention, which can be as well implemented totally in hardware, or as a mix of software and hardware. 
     Although the present invention has been disclosed and described by way of some embodiments, it is apparent to those skilled in the art that several modifications to the described embodiments, as well as other embodiments of the present invention are possible without departing from the scope thereof as defined in the appended claims. 
     In particular, albeit the invention has been disclosed making reference to the HSV color space, this is not to be intended as a limitation: the invention applies in general to any representation of the pixel properties (color, luminosity) given in a space having at least one angular coordinate. In general, an angular coordinate may need more than two directions to be properly described in terms of derived linear coordinates. 
     Also, although in the described embodiment the method of the invention has been used in the context of a digital video segmentation algorithm, this is not to be construed as a limitation: the method of the invention can be applied in many different contexts, and in general whenever a description of the pixel properties is given in a space having at least one angular coordinate, and it is necessary to calculate the average value of the angular coordinate of the pixels in a certain pixel population. 
     Moreover, although in the foregoing reference has always been made to a video frame sequence captured in real time from a videocamera, this is not to be considered as limiting the invention. In fact, the video sequence to be segmented could be an already existing video sequence, for example stored in the memory of the data processing apparatus.