Abstract:
What is disclosed is a system and method for determining whether a front seat in a motor vehicle is occupied based on seat pattern recognition. The present invention takes advantage of the observation that an unoccupied seat of a motor vehicle exhibits features which are distinguishable from an occupied seat. An unoccupied motor vehicle seat typically features long contiguous horizontal line segments and curve segments, and substantially uniform areas encompassed by these segments which are not present in an occupied seat. The present method provides a long horizontal edge test which uses location information within a defined window of the image, edge linking, softness of the edge, number of lines, line/curve fitting, and other techniques to locate horizontal edges in the image which define a seat, and a uniformity step which determines whether the area bounded by the horizontal edges is relatively uniform indicating an unoccupied seat.

Description:
TECHNICAL FIELD  
       [0001]    The present invention is directed to systems and methods which process an image of a motor vehicle to determine whether a front seat in a front passenger compartment of that vehicle is occupied. 
       BACKGROUND  
       [0002]    High Occupancy Vehicle (HOV) lane enforcement is one of the important topics in the transportation management business. In HOV lane enforcement, penalties are imposed on owners of vehicles travelling with less than a predetermined number of occupants (e.g., less than 2). Recent efforts have been directed to the areas of sensing and image capture for HOV lane enforcement. Further development in this art is needed as entirely automatic solutions for determining the number of occupants in a vehicle can be quite challenging, particularly for the passengers in the back seats. Nevertheless, semi-automatic methods that combine machine detection with human verification/inspection are valuable, as these methods reduce the workload and increase the detection rate comparing to entirely human detection. In the semi-automatic method, an alert signal, together with images/video of the vehicle, is sent to the law-enforcement officer, if an HOV lane violation is detected by the machine. The officer may verify the captured image and decide if further actions are necessary. Such methods increase the productivity of the traffic enforcement authorities. 
         [0003]    Accordingly, what is needed in this art are increasingly sophisticated systems and methods for processing an image of a motor vehicle to determine whether a front seat in a front passenger compartment of that vehicle is occupied. 
       INCORPORATED REFERENCES 
       [0004]    The following U.S. Patents, U.S. Patent Applications, and Publications are incorporated herein in their entirety by reference. 
         [0005]    “Determining A Number Of Objects In An IR Image”, U.S. patent application Ser. No. 13/086,006, by Wang et al. 
         [0006]    “Determining A Total Number Of People In An IR Image Obtained Via An IR Imaging System”, U.S. patent application Ser. No. 12/967,775, by Wang et al. 
         [0007]    “Method For Classifying A Pixel Of A Hyperspectral Image In A Remote Sensing Application”, U.S. patent application Ser. No. 13/023,310, by Mestha et al. 
         [0008]    “ Face Detection Using Local SMQT Features And Split Up SNoW Classifier ”, by Mikael Nilsson, lorgen Nordberg, and Ingvar Claesson, Blekinge Inst. of Tech., School of Eng., Ronneby, Sweden, Int&#39;l Conf. Acoustics Speech and Signal Proc. (ICASSP), 589-592, ISSN; 1520-6149, Honolulu, Hi. (April 2007). 
         [0009]    “ Face Detection By SMQT Features And SNoW Classifier Using Color Information ”, K. Somashekar, C. Puttamadappa, and D. N. Chandrappa, Int&#39;l Journal of Eng. Science and Technology (IJEST), Vol. 3, No. 2, pp. 1266-1272, (February 2011). 
       BRIEF SUMMARY  
       [0010]    What is disclosed is a novel system and method for processing an image of a motor vehicle to determine whether a front seat in a front passenger compartment of that vehicle is occupied. The present invention takes advantage of the observation that an unoccupied seat of a motor vehicle exhibits features which are distinguishable from an occupied seat. An unoccupied motor vehicle seat typically features long contiguous horizontal line segments and curve segments, and substantially uniform areas encompassed by these segments which are not present in an occupied seat. The present method provides an edge detection test to locate horizontal and vertical line segments in the image and curved segments, and a uniformity step which determines whether the area bounded by these edge segments are relatively uniform or relatively non-uniform. 
         [0011]    In one example embodiment, the present system and method for determining whether a seat in an image of a motor vehicle is occupied involves first, capturing an image of a motor vehicle which is intended to be analyzed for seat occupancy. The image is captured using, for example, a still or video imaging system operating in either the visible or infrared wavelength bands. The captured image is analyzed to determine features of a seat. In a manner more fully disclosed herein, the captured image is analyzed to extract seat features and then a determination is made whether the seat is occupied. In one embodiment, this involves performing edge detection on the image to determine line and curve segments and then analyzing the line segments to determine a boundary region of a seat and an associated headrest, if any. Once the seat features have been identified, a determination can then be made whether the seat is occupied. 
         [0012]    Also disclosed is a method for determining whether a violation has occurred by the vehicle traveling in a HOV lane which can be used in conjunction with the teachings hereof. In this embodiment, the captured image is preprocessed for contrast, noise reduction, and motion deblurring. The background is identified and the vehicle is located in the scene. A front windshield of the vehicle is isolated via feature extraction (e.g. parallel lines between the upper/lower windshield and side mirrors). Once the windshield has been isolated, face detection is performed using methods such as, for example, local SMQT features and split-up SNoW classifier as disclosed in the above-incorporated references. If there isn&#39;t a single face detected in the windshield area then the process stops and the image is sent to a human operator for manual processing. If multiple faces have been detected, no further verification is required as the vehicle contains multiple passengers. If only a single face is detected then a further determination is made whether the detected face is on the passenger side or the driver&#39;s side relative to the windshield region. If the detected face is on the passenger side of the windshield area, it is assumed to be the face of a passenger and, in one embodiment, the image is sent to an operator for processing. In another embodiment, it is assumed that there are two passengers in the vehicle and no HOV violation is recorded. If the detected face is to the driver&#39;s side of the front windshield area, it is assumed to be the driver of the vehicle and a second determination is made whether the front seat on the passenger side is occupied. Using the teachings hereof, seat features are attempted to be extracted in an identified region of the front passenger compartment where a passenger would be seated. A determination is made whether a passenger is seated in the front of the vehicle based upon the results of the seat feature extraction. If no seat features were extracted, the front passenger region of the vehicle is determined to be occupied as the seat features are blocked by the seated passenger. If seat features can be extracted then it is determined that the front passenger seat is unoccupied and a violation has occurred. Various embodiments are disclosed. 
         [0013]    Many features and advantages of the above-described method will become readily apparent from the following detailed description and accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]    The foregoing and other features and advantages of the subject matter disclosed herein will be made apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: 
           [0015]      FIG. 1  shows a front and side view of an example seat of a motor vehicle; 
           [0016]      FIG. 2  shows an example vehicle occupancy detection system which incorporates the a video imaging system and which transmits the video image to a remote device for processing in accordance with the teachings hereof; 
           [0017]      FIG. 3  is a flow diagram of one example embodiment of the present method for determining whether a seat in an image of a motor vehicle is occupied; 
           [0018]      FIGS. 4A-C  show edge profiles of typical seats, with  4 B and  4 C showing an edge with a sharp transition and a ridge edge, respectively; 
           [0019]      FIG. 5  which illustrates a block diagram of one example processing system capable of implementing various aspects of the present method shown and described with respect to the flow diagram of  FIG. 3 ; 
           [0020]      FIGS. 6A-B  illustrate a front passenger compartment of a motor vehicle from the standpoint of looking through the front windshield; 
           [0021]      FIG. 7  is a flow diagram of one example embodiment of a method for determining whether a violation has occurred by a vehicle traveling in a HOV lane; 
           [0022]      FIG. 8  is a continuation of the flow diagram of  FIG. 7  with flow processing continuing with respect to node A; and 
           [0023]      FIG. 9  shows a windshield area of a motor vehicle traveling in the HOV lane having a driver occupying the driver side of the front passenger compartment and no passenger occupying the passenger side of the front compartment. 
       
    
    
     DETAILED DESCRIPTION 
       [0024]    What is disclosed is a novel system and method for vehicle occupancy detection based on seat feature extraction. 
         [0025]    It should be appreciated that, in many countries, automobiles are designed such that the driver sits on the righthand side of the front passenger compartment and the passenger sits on the lefthand side of the front passenger compartment taken from the viewpoint of standing in the front of the vehicle and looking at the front windshield. In other countries, automobiles are designed so that the driver is on the lefthand side and the passenger is on the righthand side of the front passenger compartment from the same viewpoint. As such, any discussion herein referring to left and right side of the passenger compartment is intended to cover both designs and should not be viewed as limiting in any way. 
       Non-Limiting Definitions 
       [0026]    A “motor vehicle” can be either a passenger automobile such as, for example, the family car, or a commercial vehicle or truck. 
         [0027]    An “image of a motor vehicle” means a still image or a sequence of video images of a motor vehicle. Such an image is composed of a plurality of pixels. 
         [0028]    A “feature of a seat” refers to any physical characteristic of a seat of an automobile. Such features are the shape of the seat, i.e., size, width, height, headrest, and arm rests of the seat, the characteristics of any of the materials which comprise the seat, along with a color and texture of the seat. One example motor vehicle seat is shown and discussed with respect to seat  100  of  FIG. 1 . 
         [0029]    An “occupied seat” is a seat within a motor vehicle which has a human being sitting in that seat. An unoccupied seat does not have a human occupant. One example unoccupied seat is shown in  FIG. 1 . The embodiment of  FIG. 9  shows the front passenger seat unoccupied and the front driver&#39;s seat occupied. 
         [0030]    The “windshield area” is the area which has been clipped from an image of a motor vehicle showing the front passenger compartment of the vehicle. An example windshield area is shown at region  602  of  FIGS. 6A and 6B . 
       Basic Edge Detection 
       [0031]    Edge detection is a method for identifying areas or regions of an image at which the image changes or where discontinuities can be detected. There are many methods for edge detection. Most edge detection methods can be grouped into search-based methods and zero-crossing based methods. Search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression in terms of a gradient, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. Zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is often applied. The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions. For instance, a bilateral low-pass filter has the property of preserving edges in an image while smoothing random noise. The smoothing effect of the bilateral filter decays not only as a function of the distance from the input pixel, but also as a function of difference between the color (or intensity) values at the reference and neighboring pixels. Strong edges which exhibit large color or intensity differences will not be smoothed while small differences introduced by noise or texture will be smoothed. Essentially, the bilateral filter operates not only on a spatial distance from the input pixel but on a distance in color or intensity space from the color or intensity at the input pixel. 
       Motor Vehicle Front Passenger Seat 
       [0032]    Reference is now being made to  FIG. 1  which illustrates a front and a side view of an example seat of a motor vehicle which is unoccupied. 
         [0033]    In  FIG. 1 , seat  100  has a headrest  102  for supporting the head. In various seat configurations, headrest  102  is vertically extendable by left and right slideably retractable support braces  104  and  106 , respectively, for raising and lowering the headrest to a desired height. The slideably retractable braces which support the headrest are internally attached to back rest  108  which supports the passenger sitting in the seat. Seat  100  is shown comprising a left and right arm rest  110  and  112 , respectively, for supporting the arms. Seat cushion portion  114  carries the weight of the seat&#39;s occupant. Seat cushion  114  is fixed to support brace  116  which, in many vehicles, enables the seat&#39;s occupant to adjust the seat both horizontally (forward and backward) and vertically (raise and lower). Various aspects of the teachings hereof are directed towards determining whether such a seat in a passenger compartment of a motor vehicle contains a human occupant. 
       Example HOV Camera System 
       [0034]    Reference is now being made to  FIG. 2  which shows an example target vehicle occupancy detection system incorporating a video imaging system which transmits the video image to a remote device for processing in accordance with various embodiments of the teachings hereof. Vehicle  200  contains a human occupant  202  traveling at velocity v in a direction indicated by vector  203  along HOV lane  204 . Positioned within a desired distance d above lane  204  is support arm  205  comprising a tubular construction similar to that used for traffic lights. Fixed to this support arm is image capture system  207  having a transmission element  208  for communicating captured images to a remote device for processing as will be next described with respect to the flow diagram of  FIG. 3 . Device  207  may comprise a camera equipped with a telephoto lens, a filter, and a polarizing lens to reduce glare. It should be appreciated that the occupant of the vehicle is sitting in a seat such as that of  FIG. 1 . Also show attached to support arm  205  is illuminator  206  for illuminating motor vehicle  200  using a light source as required by camera  207 . 
       Flow Diagram of Seat Feature Extraction 
       [0035]    Reference is now being made to the flow diagram of  FIG. 3  which illustrates one example embodiment of the present method for determining whether a seat in an image of a motor vehicle is occupied using seat feature extraction. Flow processing begins at step  300  and immediately proceeds to step  302 . 
         [0036]    At step  302 , an image of a motor vehicle intended to be analyzed for front seat occupancy is received. The image can be received from a still camera or a video imaging system operating in either the visible or non-visible spectrum. In the embodiment wherein the present method of seat occupancy detection is used for HOV lane enforcement, the image of the moving motor vehicle is preferably obtained from a video imaging system positioned nearby or over the lane wherein traffic enforcement is desired. The captured images are transmitted over a wired or wireless network to a computer workstation for processing. The image may be pre-processed to determine a location of a windshield area and that portion of the image clipped for further processing. One or more regions of the image may be selected for processing by a user using, for example, a graphical display device of a computer workstation wherein the captured images are displayed. Regions of the image may be enlarged or otherwise enhanced by, for instance, adjusting brightness, contrast, hue, and the like. 
         [0037]    At step  304 , edge detection is performed on the captured image to determine line segments and curve segments associated with a vehicle front seat and headrest. Edge detection is performed using techniques such as horizontal edge detection followed by edge linking, or a Hough Transform. One embodiment utilizes a complexity measure processor which incorporates a spatially sensitive entropy operator to calculate a local measure of complexity (entropy) for a given pixel in the image. This complexity measure is then used to reference a LUT to obtain a threshold value which, in turn, is used by the spatial bilateral filter to process pixels of the image. Other local operators may be employed to determine a pixel&#39;s respective measure of complexity. A discussion as to the features, benefits, and differences of various operators is beyond the scope hereof. Suffice it to say that each operator brings different characteristics to bear on the calculation of a pixel&#39;s complexity measure. 
         [0038]    At step  306 , a determination is made whether the line and curved segments are substantially contiguous. If so then at step  308 , it is determined that the seat is unoccupied. Otherwise, at step  310 , it is determined that the seat is unoccupied. When a seat is occupied, long edges are partially (or fully) obscured by the seat&#39;s occupant. As a result, edges may be broken into shorter pieces or missing altogether. Occasionally, line segments comprising a top edge of a seat remain intact when a shorter person occupies that seat. Edges associated with seats and headrests are typically “soft” with a relatively smooth transition. A typical edge profile is shown in  FIG. 4A .  FIGS. 4B and 4C  represent an edge with a sharp transition and a ridge edge, respectively. These two types of edges are more often seen in cases where boundaries between adjoining fabrics of different colors and sewing seams. The edge profile is helpful in distinguishing seat edges from other edges. In another embodiment, pixels of the image associated with the seat and headrest areas are analyzed for color or intensity and a determination of whether the seat is occupied is based upon a uniformity of color or intensity in bounded areas. If the color or intensity of the area bounded by the line and curve segments associated with the seat and headrest is substantially uniform, then the seat is determined to be unoccupied. 
         [0039]    In one embodiment of a HOV enforcement system, an authority is alerted by a signal when a front passenger seat is not occupied and the motor vehicle is traveling in a HOV lane during a time when travel in these lanes is restricted to two or more persons. 
       Example Image Processing System 
       [0040]    Reference is now being made to  FIG. 5  which illustrates a block diagram of one example system capable of implementing various aspects of the present method shown and described with respect to the flow diagram of  FIG. 3 . 
         [0041]    A workstation  504  is placed in communication with image receiver  502  for receiving pixel values from, for instance, antenna  208  of  FIG. 2 , and for effectuating bi-directional communication between computer  504  and detection device  208 . Computer  504  further comprises display monitor  503  and user interface  505  for enabling a display of information for a user and for effectuating a user input or selection such as, for example, the user identifying a windshield area of the image or for visually inspecting an image in the instance where occupancy has failed, as discussed with respect to the flow diagram of  FIGS. 7 and 8 . A user may use the graphical user interface, e.g., keyboard and monitor, to identify or select pixels and/or areas of the image for processing or provide other user input required for the implementation hereof. Pixels and/or regions of identified or otherwise detected in the received image may be retrieved from a remote device over network  501 . Various portions of the captured image of the motor vehicle may be stored to a memory or storage device in communication with workstation  504  or may be communicated to a remote device over network  501  via a communications interface (not shown) for remote storage or further processing. Computer  504  and receiver  502  are in communication with Image Processing Unit  506 . 
         [0042]    Image Processing Unit  506  is shown comprising a buffer  507  for queuing information relating to the received image such as, for instance, regions of interest within the image, and the like, which have been selected or otherwise identified for processing. Such a buffer may be configured to further store data, edge detection methods, line segments, mathematical formulas and representations needed to facilitate processing of the image in accordance with the teachings hereof. Edge Detection Module  508  receives from data needed to perform the calculations required and performs edge detection on the received image. Module  508  provides the detected edges to Line Segment Analyzer  509  which determines line segments. Curve Analyzer  510  determines curve segments. Uniformity Analyzer  511  is used for those embodiments where a color, reflectance, and/or texture of the seat are intended to be processed for uniformity. Continuity Processor  512  determines whether the line and curve segments are substantially contiguous. Seat Occupancy Module  513  receives results from Continuity Processor  512  and determines whether a seat is occupied. Any of the modules hereof are in communication with monitor  503  to present thereon information for a user selection. Various information such as variables, and the like, are stored and/or retrieved from storage device  514 . Any of the modules and/or processing units of  FIG. 5  are in communication with storage device  514  via pathways (not shown) and may store/retrieve data, parameter values, functions, pages, records, data, and machine readable/executable program instructions required to perform their various intended functions. Each of these modules is also in communication with workstation  504  via pathways (not shown) and may further be in communication with one or more remote devices over network  501 . It should be appreciated that some or all of the functionality for any of the modules may be performed, in whole or in part, by components internal to the workstation. 
       Example Front Passenger Compartment 
       [0043]    Reference is next being made to  FIGS. 6A and 6B  which illustrate a front passenger compartment of an example motor vehicle taken from the standpoint of the front of the vehicle looking through the front windshield  602 . In  FIG. 6A , the front passenger compartment of vehicle  600  is empty and thus features are readily extracted using the teachings hereof to identify headrest  603 A and front passenger seat  603 B, as well as headrest  605 A of driver seat  605 B shown positioned directly behind steering wheel  607 . In  FIG. 6B , the front passenger seat  603 B is shown occupied by passenger  604  and the front driver&#39;s seat  605 B is occupied by driver  606 . The windshield area of the motor vehicle can be isolated and extracted from the image using, for instance, it&#39;s location relative to sideview mirrors  608 A-B and the non-glass region surrounding the windshield itself. 
       Flow Diagram of Second Embodiment 
       [0044]    Reference is now being made to the flow diagrams of  FIGS. 7-8  which collectively illustrate one example embodiment of a method for determining whether a violation has occurred by a vehicle traveling in a HOV lane. This embodiment is used in conjunction with various aspects of the seat feature extraction teachings hereof. Flow starts at  700  and immediately proceeds to step  702  wherein an image of a motor vehicle traveling in a HOV lane is received. The image is captured using, for example, the camera system of  FIG. 2 . The captured image may be preprocessed for contrast, noise reduction, and motion deblurring. In various embodiments, the background is identified and the vehicle is located in the scene. 
         [0045]    At step  704 , a front windshield area of the vehicle is isolated via feature extraction (e.g. parallel lines between the upper/lower windshield and side mirrors). An example front windshield area is shown in  FIG. 9 . Once the windshield has been isolated in the image, the windshield is clipped from the image and, at step  706 , face detection is performed such that a determination can be made whether either side of the front passenger compartment contains a passenger. Face detection can be performed using, for example, local SMQT features and split-up SNoW classifier as disclosed in the above-incorporated references. 
         [0046]    At step  708 , a determination is made whether any faces are detected in the isolated windshield area. If not then processing continues with respect to node A wherein, at step  712 , the image is flagged for further processing. Further processing may take the form of utilizing the seat feature extraction methods disclosed herein to facilitate a determination whether the seat is occupied. In which case, processing would continue from  708  to step  718  instead of step  712 . Further processing in step  712  may take the form of a human operator visually inspecting the image to determine whether a HOV violation has occurred. After the image has been flagged for further processing, processing continues with respect to node B wherein, at step  702 , a next image of a next motor vehicle traveling in the HOV lane is received for processing and the method repeats for the next received image. 
         [0047]    If faces have been detected in the windshield area at step  708  then a determination is made at step  710  whether the total number of faces detected is greater than or equal to 2. Two faces having been detected is shown by way of example at  604  and  606  of  FIG. 6B . In this instance, processing continues with respect to step  714  wherein a determination is made that no violation has occurred because more than 1 passenger was determined to be in the front passenger compartment of the vehicle traveling in the HOV lane. Thereafter, processing continues with respect to node B wherein, at step  702 , another image of a motor vehicle traveling in the HOV lane is received for processing. 
         [0048]    If, at step  710 , only a single face was detected then processing continues with respect to node A of  FIG. 8  wherein, at step  716  a determination is made whether the detected face is on the driver&#39;s side of the windshield area. If the detected face is to the driver&#39;s side of the isolated windshield area, it is assumed to be the driver of the vehicle sitting behind steering wheel  607 . Such an embodiment is shown in  FIG. 9  which illustrates the driver&#39;s face  901  having been detected in area  902  of a driver&#39;s side of windshield region  602 . If, on the other hand, the detected face is on the passenger side of the windshield region then flow continues with respect to node B wherein, at step  702 , another image of a motor vehicle traveling in the HOV lane is received for processing. Since the face detection algorithm failed to detect a second face in the isolated windshield region, the windshield area is further processed, at step  718 , using the seat feature extraction methods hereof. The seat feature extraction methods are used to identify the features of passenger seat  603 B and headrest  603 A of  FIG. 9 . 
         [0049]    At step  720 , a determination is made whether the passenger seat of the front passenger compartment of the motor vehicle is occupied based upon the extracted seat features. As is shown in  FIG. 9 , because the driver&#39;s head  901  occupies area  902  various features of the driver&#39;s headrest  605 A and seat  605 B cannot be fully detected thus leading to a determination that the seat is occupied. If the passenger seat is occupied then processing continues with respect to node C wherein, at step  714 , it is determined that no HOV violation occurred because the front passenger compartment contained two occupants. If, at step  720 , it is determined that the front passenger seat is not occupied then, at step  722 , it is determined that a violation has occurred. When a violation has occurred, local law enforcement may be notified and a visual inspection of the vehicle performed. Thereafter, flow processing continues with respect to node B wherein a next image is received for processing. In another embodiment, further processing stops. 
         [0050]    It should be appreciated that various aspects of the embodiment of the flow diagrams of  FIGS. 7-8  are intended to be used in those HOV/HOT detection systems where a violation occurs when the front passenger compartment of the motor vehicle does not contain at least two passengers. Methods for detecting passengers in the rear passenger compartment or detecting infants in a car seat positioned in a backseat of the vehicle are beyond the scope of this invention. 
         [0051]    It should be understood that the flow diagrams depicted herein are illustrative. One or more of the operations illustrated in any of the flow diagrams may be performed in a differing order. Other operations may be added, modified, enhanced, condensed, integrated, or consolidated. Variations thereof are intended to fall within the scope of the appended claims. All or portions of the flow diagrams may be implemented partially or fully in hardware in conjunction with machine executable instructions in communication with various components of such a system. 
       Various Embodiments 
       [0052]    Various modules of the embodiments hereof may designate one or more components which may, in turn, comprise software and/or hardware designed to perform the intended function. A plurality of modules may collectively perform a single function. Each module may have a specialized processor capable of executing machine readable program instructions. A module may comprise a single piece of hardware such as an ASIC, electronic circuit, or special purpose processor. A plurality of modules may be executed by either a single special purpose computer system or a plurality of special purpose computer systems in parallel. Connections between modules include both physical and logical connections. Modules may further include one or more software/hardware modules which may further comprise an operating system, drivers, device controllers, and other apparatuses some or all of which may be connected via a network. It is also contemplated that one or more aspects of the present method may be implemented on a dedicated computer system and may also be practiced in distributed computing environments where tasks are performed by remote devices that are linked through a network. 
         [0053]    One or more aspects of the methods described herein are intended to be incorporated in an article of manufacture, including one or more computer program products, having computer usable or machine readable media. For purposes hereof, a computer usable or machine readable media is, for example, a floppy disk, a harddrive, memory, CD-ROM, DVD, tape, cassette, or other digital or analog media, or the like, which is capable of having embodied thereon a computer readable program, one or more logical instructions, or other machine executable codes or commands that implement and facilitate the function, capability, and methodologies described herein. Furthermore, the article of manufacture may be included on at least one storage device readable by a machine architecture or other xerographic or image processing system embodying executable program instructions capable of performing the methodology described in the flow diagrams. 
         [0054]    Various presently unforeseen or unanticipated alternatives, modifications, variations, or improvements therein may become apparent and/or subsequently made by those skilled in the art, which are also intended to be encompassed by the following claims. Accordingly, the embodiments set forth above are considered to be illustrative and not limiting. Various changes to the above-described embodiments may be made without departing from the spirit and scope of the invention. 
         [0055]    The teachings of any printed publications including patents and patent applications, are each separately hereby incorporated by reference in their entirety.