Patent Publication Number: US-2023141515-A1

Title: System and method for stereoscopic image analysis

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of priority of U.S. Patent Application No. 63/276,800, filed Nov. 8, 2021, and entitled “SYSTEM AND METHOD FOR STEREOSCOPIC IMAGE ANALYSIS”, which is hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to stereoscopic image analysis. More specifically, the present invention relates to calibration of imaging devices, matching of elements depicted in stereoscopic imaging systems, and 3D reconstruction 
     BACKGROUND OF THE INVENTION 
     Classical stereoscopic vision systems are commonly implemented with similar imaging devices, situated on a same imaging plane, e.g., identical cameras having identical sensors, and translation being horizontal in the sensor image axes. Implementation of such stereoscopic systems may ensure relatively simple mathematical complexity for object matching process, disparity map extraction. Such implementations are common in security and surveillance systems, where the cameras and poles are fixed and static. However, in recent years, the use of cameras have been widespread, and a vast variety of cameras are in use on a daily basis in a large range of applications and systems that requires diversity of imaging devices. For example, in advanced driver-assistance systems (ADAS) vast variety of cameras and sensors may be used, each may have different characteristics and in addition, each camera may be located in a variety of selected location according to the specific vehicle considerations, e.g., inside or outside the vehicle. In such systems, the single imaging plane constraint may complicate system installation on one hand and on the other hand, disregarding it may lead to very high computational load during image analysis and reduce the mutual field of view. 
     SUMMARY OF THE INVENTION 
     It is an object of the present application to provide a hybrid stereoscopic system based on multiple, identical and/or non-identical cameras located at initially unknown locations for improving detection capabilities while reducing computational cost. 
     Embodiments of the invention may include a method for image analysis by at least one processor, as elaborated herein. According to some embodiments, the at least one processor may be configured to receive, from a first imaging device located at a first position, a first image of a scene, having a first field of view (FOV), and receive, from a second imaging device located at a second position, a second image of the scene, having a second FOV. 
     The at least one processor may calibrate the first imaging device and the second imaging device by identifying an optical flow between the first image and the second image, applying an epipolar geometric constraint on the optical flow to find a plurality of epipolar lines, determining the first position for the first imaging device and the second position for the second imaging such that the plurality of epipolar lines converge to an origin point. The at least one processor may subsequently match at least one pixel in the first image with at least one corresponding pixel in the second image by searching the corresponding pixel on an epipolar line from the plurality of epipolar lines, where the pixel in the first image and the corresponding pixel in the second image correspond to a point in a three dimensional (3D) representation of the scene. 
     According to some embodiments, the at least one processor may determine one or more coordinates of the point in the 3D representation of the scene based on the position of the pixel in the first image and the position of the corresponding pixel in the second image. 
     The at least one processor may use the one or more coordinates of the point in the 3D representation of the scene for depth estimation of the point in the scene. The at least one processor may search the corresponding pixel on an epipolar line from the plurality of epipolar lines, to reduce a search area in the second image. 
     According to some embodiments, the at least one processor may identify an optical flow between the first image and the second image by mapping a position of a pixel in the first image to a corresponding position of the pixel in the second image. 
     Embodiments of the invention may include applying the epipolar geometric constraint on the optical flow by using convolutional neural networks (CNN) methods. 
     Embodiments of the invention may include calibrating the first imaging device and the second imaging device by determining calibration parameters for the first imaging device and for the second imaging device based on the optical flow and the epipolar geometric constraint. 
     Embodiments of the invention may include calibrating the first imaging device and the second imaging device by synchronizing the first imaging device to the second imaging device. 
     Embodiments of the invention may include matching an object in the first image with a corresponding object in the second image by searching the corresponding object on an epipolar line from the plurality of epipolar lines. 
     According to embodiments of the invention, the first image of the scene may be taken, by the first imaging device, from a first point of view and the second image of the scene may be taken, by the second imaging device, from a second point of view. 
     Embodiments of the invention may include a system for image analysis. The system may include a memory and a processor configured to receive, from a first imaging device located at a first position, a first image of a scene, wherein the first imaging device having a first FOV and receive, from a second imaging device located at a second position, a second image of the scene, wherein the second imaging device having a second FOV. 
     According to embodiments of the invention, the processor may be further configured to calibrate the first imaging device and the second imaging device by identifying an optical flow between the first image and the second image, applying an epipolar geometric constraint on the optical flow to find a plurality of epipolar lines and determining the first position for the first imaging device and the second position for the second imaging such that the plurality of epipolar lines converge to an origin point. The processor may be further configured to match a pixel in the first image with a corresponding pixel in the second image by searching the corresponding pixel on an epipolar line from the plurality of epipolar lines, wherein the pixel in the first image and the corresponding pixel in the second image correspond to a point in a three-dimensional (3D) representation of the scene. 
     According to embodiments of the invention, the processor may be further configured to determine one or more coordinates of the point in the 3D representation of the scene based on the position of the pixel in the first image and the position of the corresponding pixel in the second image. 
     According to embodiments of the invention, the processor may be further configured to calibrate the first imaging device and the second imaging device by determining calibration parameters for the first imaging device and for the second imaging device based on the optical flow and the epipolar geometric constraint. 
     According to embodiments of the invention, the processor may be further configured to calibrate the first imaging device and the second imaging device by synchronizing the first imaging device to the second imaging device. 
     According to embodiments of the invention, the processor may be further configured to match an object in the first image with a corresponding object in the second image by searching the corresponding object on an epipolar line from the plurality of epipolar lines. 
     Additionally, or alternatively, embodiments of the invention may include a method of stereoscopic image processing by at least one processor. According to some embodiments, the at least one processor may receive, from a first imaging device, having a first FOV, and located at a first, initially unknown position, a first image of a scene; receiving, from a second imaging device, having a second, different FOV, and located at a second, initially unknown position, a second image of the scene. The at least one processor may calculate a plurality of flow lines in a plane of the first image, wherein each flow line represents an optical flow between a pixel of the first image and a corresponding pixel of the second image. The at least one processor may calibrate the imaging devices by determining at least one an intrinsic camera parameter, and/or parameter of relative position between the first imaging device and second imaging device, based on the calculated flow lines. 
     According to some embodiments, calibrating the imaging devices may include an iterative calibration process, that may include one or more (e.g., a plurality of) iterations. 
     Each iteration of the calibration process may include, for example calculating the flow lines, based on (a) location of the pixels in the first image and location of the corresponding pixels in the second image, and (b) at least one parameter of relative position between the first imaging device and second imaging device; and adjusting the at least one parameter of relative position, such that the flow lines may intersect at a region of convergence in a plane of the first image. The at least one processor may continue the iterative calibration process until the region of convergence may be confined to a minimal radius around a predetermined location in a plane the first image. 
     Additionally, or alternatively, each iteration further may include calculating a convergence error value, representing distance of at least one flow line from the region of convergence. The at least one processor may adjust the at least one intrinsic camera parameter and/or the at least one parameter of relative position by calculating a value of the intrinsic camera parameter and/or parameter of relative position so as to minimize the convergence error value. 
     According to some embodiments, each pair of consecutive iterations may include (i) a first iteration, which that includes adjustment of at least one parameter of relative position and/or intrinsic camera parameter, and (ii) a second iteration, which may include adjustment of at least one other parameter of relative position and/or intrinsic camera parameter. 
     The parameter of relative position may include, for example a translation between the first imaging device and second imaging device, and/or a difference in orientation between the first imaging device and second imaging device. 
     According to embodiments of the invention, the at least one processor may triangulate between one or more pixels depicted in the first image and one or more corresponding pixels depicted in the second image, based on (a) location of the one or more pixels in the first image, (b) location of the one or more corresponding pixels in the second image, and (c) the at least one determined parameter of relative position. The at least one processor may subsequently obtain 3D coordinates of one or more respective points in the scene, based on said triangulation. 
     Additionally, or alternatively, the at least one processor may produce a 3D representation of the scene based on the 3D coordinates of the one or more points in the scene. 
     According to embodiments of the invention, the at least one processor may analyze at least one of the first image and the second image to produce, based on the plurality of flow lines, a respective plurality of epipolar lines having a common origin point. The common origin point may correspond to the region of convergence in the first image. 
     This analysis may include, for example, applying an image rectification function on the first image and on the second image, to produce respective first rectified image and second rectified image, wherein the rectified images may be characterized by having a minimal level of image distortion, thereby aligning the flow lines of the first image into straight, epipolar lines that intersect at the common origin point in a plane of the first rectified image. Each epipolar line may represent an optical flow between a pixel of the first rectified image and a corresponding pixel of the second rectified image. 
     Additionally, or alternatively, the at least one processor may select a first pixel in the first rectified image; identify an epipolar line that connects the first pixel with the common origin point in the first rectified image; identify a subset of pixels in the second rectified image that pertain to a location defined by the determined epipolar line in the first rectified image; and selecting a second pixel among the subset of pixels as matching the first pixel of the first rectified image, based on a predetermined similarity metric. 
     In other words, the at least one processor may match one or more pixels in the first rectified image with one or more corresponding pixels in the second rectified image, by searching the one or more corresponding pixels along an epipolar line of the plurality of epipolar lines. 
     Additionally, or alternatively, the at least one processor may apply an object-detection algorithm on the first rectified image to identify an object depicted in the first image; and match the detected object in the first image with a corresponding object in the second rectified image by searching the corresponding object along an epipolar line of the plurality of epipolar lines. 
     According to some embodiments, the calibration of imaging devices may be performed repeatedly over time. At each repetition, the first imaging device and the second imaging device may be synchronized, so as to produce respective images of the scene substantially at the same time. 
     According to some embodiments, the at least one processor may calculate a flow line by applying a machine-learning (ML) model on the first image and the second image, to map between a position of a first pixel in the first image and a position of the corresponding pixel in the second image. 
     According to some embodiments, the at least one processor may producing at least one notification pertaining to the 3D coordinates of the one or more points in the scene; and transmit that notification to at least one processor of an Advanced Driver Assisting Systems (ADAS) in a vehicle. The ADAS processor may, in turn, be configured to display said notification in a user interface (UI) of the ADAS. 
     Additionally, or alternatively, the at least one processor may transmit the notification to at least one controller of a vehicle, configured to control one or more motors or actuators, of the vehicle, so as to conduct the vehicle based on the notification. 
     Embodiments of the invention may include a method for image analysis by at least one processor. Embodiments of the method may include receiving, from a first imaging device, having a first FOV, and located at a first, initially unknown position, a first image of a scene; receiving, from a second imaging device, having a second, different FOV, and located at a second, initially unknown position, a second image of the scene; calibrating at least one of the first imaging device and second imaging device, to obtain an origin point in a plane of the first image, said origin point defining convergence of a plurality of epipolar lines, each representing an optical flow between the first image and the second image; and matching one or more pixels in the first image with one or more corresponding pixels in the second image by searching the one or more corresponding pixels along an epipolar line of the plurality of epipolar lines. 
     Embodiments of the invention may include a system for calibrating imaging devices. Embodiments of the system may include a first imaging device, having a first FOV, and located at a first, initially unknown position; a second imaging device, having a second, different FOV, and located at a second, initially unknown position; a non-transitory memory device, wherein modules of instruction code may be stored, and at least one processor associated with the memory device, and configured to execute the modules of instruction code. 
     Upon execution of the modules of instruction code, the at least one processor may be configured to: receive a first image of a scene from the first imaging device, and receive a second image of the scene from the second imaging device; calculate a plurality of flow lines in the first image, wherein each flow line represents an optical flow between a pixel of the first image and a corresponding pixel of the second image; and calibrate the imaging devices by determining at least one parameter of relative position between the first imaging device and second imaging device, based on the calculated flow lines. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings in which: 
         FIG.  1    is a block diagram, depicting a computing device which may be included in a system for stereoscopic image processing according to some embodiments of the invention; 
         FIG.  2    is a schematic diagram depicting terms and principles of epipolar geometry, in a constellation of two cameras, as known in the art; 
         FIGS.  3 A,  3 B and  3 C  are block diagrams depicting exemplary aspects of a system for stereoscopic image analysis, according to some embodiments of the invention; 
         FIGS.  4 A- 4 F , are pictorial illustrations of an exemplary system that may include two imaging devices (e.g., cameras), and images taken by that system, according to embodiments of the invention:  FIG.  4 A  depicts an image captured by a first camera of the system,  FIG.  4 B  depicts an image captured by a second camera of the system,  FIG.  4 C  is an illustrative depiction of an optical flow, between the images depicted in  FIG.  4 A  and  FIG.  4 B ,  FIG.  4 D  depicts a ground-truth 3D representation of the captured scene,  FIG.  4 E  depicts a calculated 3D representation that may be generated according to embodiments of the invention, and  FIG.  4 F  depicts an exemplary setup of cameras of the system on a platform such as a car or vehicle; 
         FIG.  5    is a schematic diagram depicting terms and principles of bundle adjustment calibration, as known in the art; 
         FIG.  6 A  is a pictorial illustration of flow lines before a calibration process, according to some embodiments of the invention; 
         FIG.  6 B  is a pictorial illustration of flow lines and/or epipolar lines after a calibration process and image rectification, according to some embodiments of the invention; 
         FIGS.  7 A and  7 B  are pictorial illustrations depicting bird-eye view of examples for stereoscopic imaging systems, undergoing single plane rectification, and a parallel plane rectification, respectively; 
         FIG.  8 A  is a flowchart depicting a method of image analysis, according to some embodiments of the invention; 
         FIG.  8 B  is a flowchart depicting another method of image analysis, according to some embodiments of the invention; and 
         FIG.  8 C  is a flowchart depicting yet another method of image analysis, according to some embodiments of the invention. 
     
    
    
     It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. 
     DETAILED DESCRIPTION OF THE PRESENT INVENTION 
     One skilled in the art will realize the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting of the invention described herein. Scope of the invention is thus indicated by the appended claims, rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. 
     In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention. Some features or elements described with respect to one embodiment may be combined with features or elements described with respect to other embodiments. For the sake of clarity, discussion of same or similar features or elements may not be repeated. 
     Although embodiments of the invention are not limited in this regard, discussions utilizing terms such as, for example, “processing,” “computing,” “calculating,” “determining,” “establishing”, “analyzing”, “checking”, or the like, may refer to operation(s) and/or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulates and/or transforms data represented as physical (e.g., electronic) quantities within the computer&#39;s registers and/or memories into other data similarly represented as physical quantities within the computer&#39;s registers and/or memories or other information non-transitory storage medium that may store instructions to perform operations and/or processes. 
     Although embodiments of the invention are not limited in this regard, the terms “plurality” and “a plurality” as used herein may include, for example, “multiple” or “two or more”. The terms “plurality” or “a plurality” may be used throughout the specification to describe two or more components, devices, elements, units, parameters, or the like. The term “set” when used herein may include one or more items. 
     Unless explicitly stated, the method embodiments described herein are not constrained to a particular order or sequence. Additionally, some of the described method embodiments or elements thereof can occur or be performed simultaneously, at the same point in time, or concurrently. 
     Reference is now made to  FIG.  1   , which is a block diagram depicting a computing device, which may be included within an embodiment of a system for image analysis, according to some embodiments. 
     Computing device  1  may include a processor or controller  2  that may be, for example, a central processing unit (CPU) processor, a chip or any suitable computing or computational device, an operating system  3 , a memory  4 , executable code  5 , a storage system  6 , input devices  7  and output devices  8 . Processor  2  (or one or more controllers or processors, possibly across multiple units or devices) may be configured to carry out methods described herein, and/or to execute or act as the various modules, units, etc. More than one computing device  1  may be included in, and one or more computing devices  1  may act as the components of, a system according to embodiments of the invention. 
     Operating system  3  may be or may include any code segment (e.g., one similar to executable code  5  described herein) designed and/or configured to perform tasks involving coordination, scheduling, arbitration, supervising, controlling or otherwise managing operation of computing device  1 , for example, scheduling execution of software programs or tasks or enabling software programs or other modules or units to communicate. Operating system  3  may be a commercial operating system. It will be noted that an operating system  3  may be an optional component, e.g., in some embodiments, a system may include a computing device that does not require or include an operating system  3 . 
     Memory  4  may be or may include, for example, a Random-Access Memory (RAM), a read only memory (ROM), a Dynamic RAM (DRAM), a Synchronous DRAM (SD-RAM), a double data rate (DDR) memory chip, a Flash memory, a volatile memory, a non-volatile memory, a cache memory, a buffer, a short term memory unit, a long term memory unit, or other suitable memory units or storage units. Memory  4  may be or may include a plurality of possibly different memory units. Memory  4  may be a computer or processor non-transitory readable medium, or a computer non-transitory storage medium, e.g., a RAM. In one embodiment, a non-transitory storage medium such as memory  4 , a hard disk drive, another storage device, etc. may store instructions or code which when executed by a processor may cause the processor to carry out methods as described herein. 
     Executable code  5  may be any executable code, e.g., an application, a program, a process, task, or script. Executable code  5  may be executed by processor or controller  2  possibly under control of operating system  3 . For example, executable code  5  may be an application that may perform image analysis as further described herein. Although, for the sake of clarity, a single item of executable code  5  is shown in  FIG.  1   , a system according to some embodiments of the invention may include a plurality of executable code segments similar to executable code  5  that may be loaded into memory  4  and cause processor  2  to carry out methods described herein. 
     Storage system  6  may be or may include, for example, a flash memory as known in the art, a memory that is internal to, or embedded in, a micro controller or chip as known in the art, a hard disk drive, a CD-Recordable (CD-R) drive, a Blu-ray disk (BD), a universal serial bus (USB) device or other suitable removable and/or fixed storage unit. Data pertaining to one or more images may be stored in storage system  6  and may be loaded from storage system  6  into memory  4  where it may be processed by processor or controller  2 . In some embodiments, some of the components shown in  FIG.  1    may be omitted. For example, memory  4  may be a non-volatile memory having the storage capacity of storage system  6 . Accordingly, although shown as a separate component, storage system  6  may be embedded or included in memory  4 . 
     Input devices  7  may be or may include any suitable input devices, components, or systems, e.g., a detachable keyboard or keypad, a mouse and the like. Output devices  8  may include one or more (possibly detachable) displays or monitors, speakers and/or any other suitable output devices. Any applicable input/output (I/O) devices may be connected to Computing device  1  as shown by blocks  7  and  8 . For example, a wired or wireless network interface card (NIC), a universal serial bus (USB) device or external hard drive may be included in input devices  7  and/or output devices  8 . It will be recognized that any suitable number of input devices  7  and output device  8  may be operatively connected to Computing device  1  as shown by blocks  7  and  8 . 
     A system according to some embodiments of the invention may include components such as, but not limited to, a plurality of central processing units (CPU) or any other suitable multi-purpose or specific processors or controllers (e.g., similar to element  2 ), a plurality of input units, a plurality of output units, a plurality of memory units, and a plurality of storage units. 
     The term neural network (NN) or artificial neural network (ANN), e.g., a neural network implementing a machine learning (ML) or artificial intelligence (AI) function, may be used herein to refer to an information processing paradigm that may include nodes, referred to as neurons, organized into layers, with links between the neurons. The links may transfer signals between neurons and may be associated with weights. A NN may be configured or trained for a specific task, e.g., pattern recognition or classification. Training a NN for the specific task may involve adjusting these weights based on examples. Each neuron of an intermediate or last layer may receive an input signal, e.g., a weighted sum of output signals from other neurons, and may process the input signal using a linear or nonlinear function (e.g., an activation function). The results of the input and intermediate layers may be transferred to other neurons and the results of the output layer may be provided as the output of the NN. Typically, the neurons and links within a NN are represented by mathematical constructs, such as activation functions and matrices of data elements and weights. At least one processor (e.g., processor  2  of  FIG.  1   ) such as one or more CPUs or graphics processing units (GPUs), or a dedicated hardware device may perform the relevant calculations. 
     Reference is now made to  FIG.  2   , which is a schematic diagram depicting terms and principles of epipolar geometry, in a constellation of two cameras, as known in the art. In the example of  FIG.  2   , points Cleft and Cright represent projection centers of a left camera and a right camera of the camera constellation, respectively. The projection center of each camera may be understood as identifying an origin of a three dimensional (3D) reference frame of that camera. 
     The vectors Pleft and Pright may represent a line of sight that connects each camera&#39;s projection center with a point of interest or target P in the depicted 3D space, and are projected onto the cameras&#39; image planes at points pl and pr respectively. 
     As shown in the example of  FIG.  2   , points P, Cleft and Cright define a plane that is herein referred to as an “epipolar plane”. The intersection of this epipolar plane with each camera&#39;s image plane defines a respective line, which is referred to herein as an epipolar line. 
     The epipolar lines are characterized as the geometric locations along which a point in the 3D space (e.g., point P), that is depicted in a first image plane (e.g., at location pl) and may be expected to be found in the complementary image plane (e.g., at location pr). This mapping between points in the left image and lines in the right image (and vice versa) may be referred to herein as an “epipolar constraint”. 
     The points at which the line that connects the centers of projection (Cleft, Cright) intersects the image planes are called epipoles. 
     In the example of  FIG.  2   , it is assumed that the location or position of each camera (or at least the relative location between the two cameras) is well known. In such a constellation, since all lines of sight (e.g., Pleft) include the projection center (e.g., Cleft), then all epipolar lines of each camera converge onto that camera&#39;s epipole. 
     Embodiments of the invention may be configured to receive images that originate from imaging devices or cameras of which the relative location or position of the centers of projection is initially unknown. Additionally, or alternatively, imaging devices or cameras of the present invention may have different intrinsic parameters  120 B such as different focal lengths, fields of view (FOV), and image distortion. In other words, embodiments of the invention may operate in conditions where the epipolar constraint may not be initially exploited, or applied directly to raw images acquired from the imaging devices, to predict a location of a target in a first image plane, given its projection on the complementary image plain. 
     As elaborated herein, embodiments of the invention may calculate a plurality of flow lines in a plane of the first image  20 A′. Each flow line may represent an optical flow between a pixel of a first image, acquired from a first camera, and a corresponding pixel of a second image, acquired from a second camera. Embodiments of the invention may then perform a process of calibration of at least one camera, based on the flow lines, to determine position parameters of the at least one camera (or the relative location between the two cameras), thereby determining epipoles of the participating imaging devices. Embodiments of the invention may subsequently utilize the epipolar constraints as explained in relation to  FIG.  2   , to predict a location of a target in a first image plane, given its projection on the complementary image plain. 
     Reference is now made to  FIG.  3 A , which is a block diagram depicting a system  10  for stereoscopic image analysis, according to some embodiments of the invention. 
     According to some embodiments of the invention, system  10  may be implemented as a software module, a hardware module, or any combination thereof. For example, system  10  may be, or may include a computing device such as element  1  of  FIG.  1   , and may be adapted to execute one or more modules of executable code (e.g., element  5  of  FIG.  1   ) to implement methods of stereoscopic image analysis, as further described herein. 
     As shown in  FIG.  3 A , arrows may represent flow of one or more data elements to and from system  10  and/or among modules or elements of system  10 . Some arrows have been omitted in  FIG.  3    for the purpose of clarity. 
     According to some embodiments, system  10  may be configured to implement methods that improve performance of stereoscopic vision algorithms in relation to currently available systems of stereoscopic image analysis. 
     For example, and as known in the art, currently available systems of stereoscopic image analysis are typically implemented using identical cameras, identical sensors, sensors with parallel focal planes, cameras having fixed and static poles and the like. In contrast, embodiments of the invention may facilitate stereoscopic image analysis while using imaging devices that have different, or even undetermined optical characteristics and positioning settings, and may be utilized in applications which require or include mobility or movements of the participating cameras. 
     For example, system  10  may facilitate stereoscopic image analysis in applications that require installation of various types of cameras inside or around a common platform  200 , such as a vehicle, e.g., for facilitating Advanced Driver Assisting Systems (ADAS) functionality, for controlling autonomous driving vehicles, and/or other implementations in which a parallelism constraint on cameras installation may complicate the installation and/or restrict it. In such implementations it may be desirable to fix the two or more cameras in selected points, e.g., inside and/or outside a vehicle, not do not necessarily align the imaging devices in parallel image planes. 
     As shown in the example of  FIG.  3 A , system  10  may be a hybrid stereoscopic system, in the sense that it may be associated with, or may be communicatively connected to at least two different imaging devices, cameras or sensors  20  (denoted  20 A and  20 B). Additionally, or alternatively, system  10  may include the at least two cameras or sensors  20 . Imaging devices  20 A and  20 B may have different optical characteristics. For example, cameras  20  may be integrated with day sensors, IR sensors, bolometric sensors, short-wave infrared (SWIR) sensors, near infrared (NIR) sensors or any other combination of different cameras and/or sensors. In this context, the terms “imaging device”, “camera” and “sensor” may be used herein interchangeably. 
     According to some embodiments of the invention, multiple cameras  20  may be located without constraints. In other words, the exact pose, location, orientation, etc. of each camera  20 A,  20 B (and/or the relative pose, location, orientation, etc. between cameras  20 ) may initially be undetermined, such that reduction of mathematical complexity in various processes such as calibration, synchronization, disparity estimation, common FOV and depth estimation may not be available. 
     As elaborated herein, system  10  may include a practical application of calibrating between the at least two imaging devices  20 , to reduce computational complexity, and thus provide an improvement in technology of stereoscopic imaging systems. In some embodiments, system  10  may perform a calibration process based on optical flow between images  20 ′ (e.g.,  20 A′ and  20 B′) originating from cameras  20  (e.g., from cameras  20 A and  20 B respectively), instead of applying currently available methods of calibration, which typically consume heavier computational resources, such as time, memory, processing cycles and electrical power. 
     Additionally, or alternatively, and as elaborated herein, system  10  may utilize the calibration between cameras  20 , and apply epipolar geometry during a matching process, to match between a point, pixel or object in a first image  20 A′ of first camera  20 A and a corresponding point, pixel or object in an image  20 B′ of second camera  20 B. 
     As shown in the example of  FIG.  3 A , system  10  may include a synchronization module  150 . Synchronization module  150  may be configured to ensure synchronization between the plurality of cameras  20  that are positioned such as to capture or analyze concurrent images  20 A′,  20 B′ of the same scene. 
     For example, synchronization module  150  may enforce synchronization between two cameras  20  (e.g.,  20 A and  20 B) mounted on a single vehicle. The synchronization process may allow accurate detection and matching of objects in the FOV of cameras  20 . In embodiments where moving objects are detected (e.g., moving in relation to cameras  20 ), a synchronization between the multiple of cameras may be required. Synchronization module  150  may ensure that the two or more imaging devices  20  may operate at the same times and/or at the same rate. Additionally, or alternatively, synchronization module  150  may ensure that system  10  may use substantially concurrent images  20 A′,  20 B′ to implement the processes of stereoscopic image analyses, as elaborated herein. For example, synchronization module  150  may ensure that each of the plurality of cameras  20  may capture images at certain times and at a predetermined capturing rate. Synchronization module  150  may perform mutual synchronization by dedicated hardware and/or software. Additionally or alternatively, a timestamp may be attached to each of the frames or images  20 ′ by each of cameras  20 , to allow post collection synchronization. 
     As elaborated herein, imaging devices or cameras (e.g.,  20 A,  20 B) may be installed, placed, or fixed to any desired positions (e.g., on a rear side and a front side of a car). Therefore, as shown in  FIG.  3 A , system  10  may include an image preprocessing module  140 , configured to perform one or more steps or operations of image preprocessing, according to specific embodiments or implementations of the invention. It should be understood that some of these image preprocessing operations may be performed while others may be skipped, according to the specific implementation, the specific positioning of cameras  20 , the intrinsic parameters and optical characteristics  120 B of cameras  20 , and the like. Preprocessing module  140  may perform the steps of image preprocessing in parallel, or sequentially in any preferred order. 
     For example, camera  20 B may be a fisheye camera, located at the front of a platform  200 , e.g., on the bumper of vehicle  200 , to provide an FOV of forward 180 degrees for near objects&#39; detection. Camera  20 A may be located or positioned at the rear part of vehicle  200 , and may be a conventional camera for longer ranges, e.g., a pinhole camera. Preprocessing module  140  may crop at least one of images  20 A′ and  20 B′ to ensure overlapping of FOVs of the two or more images  20 ′ imaging devices  20 . 
     In another example, one or more imaging devices  20  may render a distorted or partial image of the scene. In such embodiments, preprocessing module  140  may process image  20 ′ by a dedicated image analysis algorithm  140 , e.g., a software module or application for correcting such a distortion. For example, a fisheye camera (e.g.,  20 B) may render a distorted image, characterized by a barrel distortion, as commonly referred to in the art. Preprocessing module  140  may process image  20 B′ by a dedicated image analysis algorithm or application to correct this distortion, as known in the art. 
     In another example, preprocessing module  140  apply additional image processing algorithms to mitigate differences between images  20 A′ and  20 B′, including for example differences in pixel size, image resolution, image colour (e.g., hue and saturation), brightness, contrast, aspect ratio, glare, and the like. 
     Processes and procedures included in embodiments of the invention may specifically improve ADAS systems  300  and any application or implementation that include motion, for example, autonomous driving vehicles  200  by simplifying and improving the process of camera installation. For example, embodiments of the invention may render use of a rigid rig for camera installation redundant, allowing car producers to select any desired location for camera installation. Additionally or alternatively system  10  may enable usage of any FOV, e.g., near FOV and far FOV, with any required resolution, without any constraint of sensor  20  similarity. 
     Reference is also made to  FIGS.  4 A- 4 F  which are pictorial illustrations of an exemplary installation of system  10 , and images taken by system  10 , according to embodiments of the invention. As elaborated herein, embodiments of the invention may include locating two or more imaging devices  20 A,  20 B, having different optical characteristics at two or more locations which may not be stereo-parallel, e.g., when two imaging devices view a 3D scene from two distinct positions which are not parallel, namely when the focal planes of the two imaging devices are not parallel. 
       FIG.  4 F  depicts an exemplary setup of cameras  20  (e.g.,  20 A,  20 B) on a platform  200  such as a car or vehicle  200 . Cameras  20 A and  20 B each have different optical characteristics, and may be located on different locations on vehicle  200 . Camera  20 A and camera  20 B may be located in non-parallel locations, e.g., non-stereo parallel where their focal planes are not parallel planes. For example, a large FOV camera (e.g., camera  20 B in  FIG.  4 F ) may be placed, positioned, or located in the front part of vehicle (e.g., car)  200 , e.g., for capturing objects located in proximity to vehicle  200  while a large focal length camera (e.g., camera  20 A in  FIG.  4 F ) may be placed, positioned, or located in the rear part of car  200 , e.g., for capturing objects located far away from car  200 . 
     According to embodiments of the invention, camera  20 A having a first FOV may capture a first image  20 A′ of a scene while being located at a first position (e.g., at a rear side of a vehicle  200 ). A second imaging device, e.g., camera  20 B having a second FOV may capture a second image  20 B′ of the scene while being located at a second position (e.g., at a front side of vehicle  200 ). 
       FIG.  4 A  depicts an image  20 A′ captured or taken by camera  20 A while  FIG.  4 B  depicts an image  20 B′ captured or taken by camera  20 B. In some embodiments of the invention, camera  20 A and camera  20 B may capture a 3D scene from two distinct, non-parallel positions, therefore a plurality of geometric relations may exist between the 3D points of the captured 3D scene and their projections onto each of the two-dimensional (2D) images  20 ′ captured by each of the cameras. This may lead to one or more constraints, restrictions or limitations between corresponding points or pixels of the captured images  20 ′. 
       FIG.  4 C  is an illustrative depiction of an optical flow, between the images  20 A′ and  20 B′ depicted in  FIG.  4 A  and  FIG.  4 B  respectively. 
     As known in the art, the term “optical flow” may refer to a pattern of apparent motion or change of objects, surfaces, and edges in a visual scene that may be caused by relative motion between an observer and a scene. In the context of the present invention, and as shown by arrows  110 A in  FIG.  4 C , an optical flow may refer to an alignment or association between at least one (e.g., each) pixel in image  20 A′ taken by camera  20 A (e.g., depicted in  FIG.  4 A ) and at least one (e.g., each) counterpart or corresponding pixel in image  20 B′ taken by camera  20 B (e.g., depicted in  FIG.  4 B ). 
     As shown in  FIG.  3 A , system  10  may include an optical flow module  110 , configured to determine an optical flow  110 A data element (or “optical flow  110 A” for short). Optical flow  110 A may for example be a table that represents data flow between images  20 A′ and  20 B′. In other words, optical flow  110 A may match between 2D locations or pixels in image  20 A′ and corresponding 2D locations or pixels in image  20 B′. The term “corresponding” may be used in this context to indicate regions in images  20 A′ and  20 B′ that depict of describe the same regions in the scene. Optical flow module  110  may produce optical flow  110 A by associating one or more pixels or regions in image  20 A′ taken by camera  20 A to one or respective pixels or regions in image  20 B′ taken by camera  20 B, as shown by arrows  110 A of  FIG.  4 C . 
     According to some embodiments, optical flow module may be, or may include a machine-learning (ML) model  111 , configured to map between a position of a first pixel in image  20 A′ and a position of a corresponding pixel in image  20 B′. 
     For example, ML model  111  may be implemented as at least one artificial neural network (ANN) such as a Convolutional Neural Network (CNN), that may receive images  20 A′ and  20 B′ as input, and produce optical flow  110 A as output. In some embodiments, ML model  111  may be pretrained based on annotated examples of pairs of corresponding regions or pixels (e.g., received via input device  7  of  FIG.  1   ), where one region or pixel each originating from a different image  20 ′. 
     Additionally, or alternatively, optical flow module may calculate one or more (e.g., a plurality) of flow lines  110 B in a first image (e.g.,  20 A′), wherein each flow line represents an optical flow  110 A between a pixel of the first image (e.g.,  20 A′) and a corresponding pixel of the second image (e.g.,  20 B′). In some embodiments, flow lines  110 B may be calculated as extensions of 2D lines that connect between matching optical flow 2D locations, in the first image (e.g.,  20 A′), as depicted in the illustrative example of  FIG.  4 C . 
     Reference is also made to  FIGS.  6 A, and  6 B .  FIG.  6 A  is a pictorial example of flow lines  110 B, on a first image  20 ′ (e.g.,  20 A′) before a calibration process, e.g., when imaging devices are not calibrated.  FIG.  6 B  is a pictorial example of flow lines  110 B and/or subsequent epipolar lines  180 B, on image  20 ′ (e.g.,  20 A′), which may be obtained by system  10  after a calibration process, and a subsequent optional image rectification process as elaborated herein, according to some embodiments of the invention. 
     According to some embodiments, system  10  may include a calibration module  120 , adapted to calibrate imaging devices  20  by determining at least one parameter of relative position  120 A between the first imaging device  20 A and second imaging device  20 B. As elaborated herein, calibration module  120  may perform this calibration process based on the calculated flow lines  110 B, thereby reducing consumption of computational resources in comparison to currently available methods of stereoscopic system calibration, such as bundle adjustment calibration. 
       FIG.  5    is a schematic diagram which depicts terms and principles of a process of bundle adjustment calibration, as known in the art. 
     As known in the art, bundle adjustment calibration is a process by which (a) 3D coordinates describing the scene, (b) relative positioning parameters (e.g.,  120 A) between cameras  20 , and (c) intrinsic optical characteristics  120 B of cameras (e.g.,  20 ) may be calculated, based on acquired images ( 20 ′) of a scene, acquired from different angles. As shown in  FIG.  5   , bundle adjustment calibration requires tracing of light rays originating from each feature (denoted P) in the scene, and converging on each camera&#39;s optical center (denoted O 1  and O 2 ), to calculate a reprojection error. The reprojection error defines a difference between the image locations of observed and predicted image points (depicted in  FIG.  5    as two points in each of images  1  and  2 ). The bundle adjustment calibration process seeks to minimize the reprojection errors, by iteratively optimizing, or calculating (a-c) above. 
     As explained in relation to  FIG.  5   , currently available methods of calibration must iteratively traverse over a large number of data points, each relating to specific 3D coordinates, and dependent upon a plurality of intrinsic camera parameters  120 B. It may be appreciated by a person skilled in the art, that such processes are computationally intensive, and may even become prohibitively complicated. This is especially relevant in applications where the scene may continuously change (e.g., automotive applications). According to some embodiments, calibration module  120  may perform this calibration process based on the calculated flow lines  110 B, i.e., based on 2D geometry, and in relation to a limited number of extrinsic, relative position parameters  120 A, and may therefore greatly simplify the calibration of stereoscopic imaging systems  10 . 
     The terms “intrinsic camera parameter” or “intrinsic parameter” may be used herein to indicate parameters  120 B of cameras  20  that pertain to a structure of a specific camera or imaging device, including for example a focal length, a field of view, an image distortion, and the like. 
     The terms “extrinsic camera parameter” or “extrinsic parameter” may be used herein to indicate parameters of cameras  20  that pertain to a camera&#39;s inclusion or integration into system  10 , including for example a physical location of the camera (e.g., represented by cartesian coordinates [X, Y, and/or Z]), orientation of the camera (e.g., in the Pitch, Yaw and/or Roll axes), and the like. 
     The term “relative position parameter  120 A” may be used herein to indicate a relation between extrinsic parameters of cameras  20 A and  20 B, including for example, a translation (e.g., a difference in coordinates [X, Y, and/or Z]) between cameras  20 A and  20 B, a difference in orientation (e.g., difference in Pitch, Yaw and/or Roll axes) between cameras  20 A and  20 B, and the like. 
     In another example, relative position parameter  120 A may include at least one of a length and/or an orientation of a translation vector, defined by location of cameras  20 A and  20 B. Such embodiments may be particularly useful, for example, in applications of system  10  on moving or shaky platforms  200 , such as cars: in such implementations, a distance between cameras  20 A and  20 B may be kept substantially the same, whereas the orientation of the translation vector may change significantly over time. 
     According to some embodiments, calibration module  120  may be configured to calibrate the imaging devices in an iterative calibration process. Each iteration of the calibration process may include prediction or estimation of value of a relative position parameter  120 A, and a complementary update of one or more flow lines, based on the relative position parameter  120 A value. 
     Additionally, or alternatively, each iteration of the calibration process may include (a) calculation (in a first iteration), or re-calculation (in subsequent iterations) of flow lines  110 B, based on (i) location of the pixels in the first image and location of the corresponding pixels in the second image (e.g., as depicted in  FIG.  4 C ), and (ii) at least one parameter of relative position  120 A between the first imaging device (e.g.,  20 A) and second imaging device (e.g.,  20 B). For example, in an initial iteration, flow lines  110 B may be calculated as a line connecting between pairs of pixels  110 A (received from optical flow module  110 ), and in subsequent iterations flow lines  110 B may be calculated while considering a virtual movement or modification of imaging devices  20 A/ 20 B, according to the calculated calibration parameters  120 A/ 120 B. In some embodiments such calculation may include, for example using a RANSAC (Random Sample Consensus) algorithm, in which a subset of the flow lines may be chosen to estimate the calibration parameters, and subsequently the error on all flow lines may be calculated based on the selected lines, thereby reducing the influence of outliers. 
     Each iteration of the calibration process may further include adjusting the at least one parameter of relative position  120 A, such that flow lines  110 B pass through, or intersect at a region of convergence (ROC)  110 C in the first image  20 A′ or in a plane of the first image  20 A′, as depicted in the examples of  FIGS.  4 C and  5 B . In other words, the at least one parameter of relative position  120 A may be adjusted such that under a closed set of constraints, the majority of the flow lines  110 B may converge to the minimal convergence area. 
     It may be appreciated that ROC  110 C may initially be located beyond a matrix of pixels that represent first image  20 A′, and may be moved into that matrix of pixels as part of an iterative calibration process, as elaborated herein. In this context, the term “plane” may be used to indicate a theoretic 3D spatial plane, which may be spatially aligned or converged with image  20 A′, and may continue image  20 A′ to include the location of ROC  110 C. 
     Calibration module  120  may be configured to continue the iterative calibration process until a predefined stop condition is met. 
     For example, calibration module  120  may continue the iterative calibration process, and recalculate flow lines  110 B until ROC  110 C is confined to a minimal radius around a point, or a predetermined location in first image  20 A′. In other words, the iterative calibration process may continue until one or more (e.g., all) flow lines  110 B pass near predetermined location in a plane of first image  20 A′, in a distance that does not exceed a predefined radius. 
     Additionally, or alternatively, calibration module  120  may continue the iterative calibration process, and recalculate flow lines  110 B until ROC  110 C reaches a minimal size (e.g., no longer converges into an increasingly smaller area or radius). 
     Additionally, or alternatively, the iterative calibration process may include calculation of at least one convergence error  110 D value, representing distance of at least one flow line  110 B from the region of convergence  110 C. 
     For example, as depicted in  FIG.  6 A , a first point (denoted P 1 ), may represent a pixel depicting a specific object in first image  20 A′, and a second point (denoted P 2 ), may represent location of a corresponding pixel (e.g., depicting the same object), in second image  20 B′, as obtained from optical flow module  110 . A flow line  110 B may define the line between these points in image  20 A′. A second line, denoted L 1  may represent an extension of a line that connects P 1  with a center of region of convergence  110 C at its current location (e.g., in the current iteration). In this example, a convergence error  110 D pertaining to at least one flow line  110 B may be defined by a metric or a function (e.g., sum) of distances of the at least one flow lines  110 B from region of convergence  110 C. 
     In such embodiments, at each iteration of the calibration process, calibration module  120  may calculate or adjust a value of a parameter of relative position  120 A to minimize convergence error value  110 D. 
     Pertaining to the example of  FIG.  6 A , the parameter of relative position  120 A may be an orientation of camera  20 B. It may be appreciated that the calculated adjustment of the value of parameter  120 A may alter location P 2  (that represents a pixel of image  20 B′) in image  20 A′, thereby changing location of flow line  110 B and/or convergence region  110 C in image  20 A′, to minimize convergence error value  110 D. 
     Additionally, or alternatively, each iteration may be dedicated to, or apply changes to a different set of parameters of relative position  120 A. In other words, each pair of consecutive iterations may include a first iteration, in which calibration module  120  may adjust at least one first parameter of relative position  120 A (e.g., relative translation between cameras  20  in the X axis), and a second iteration, in which calibration module  120  may adjust at least one second, different parameter of relative position  120 A (e.g., relative orientation between cameras  20  in the pitch axis). 
     According to some embodiments, calibration module may perform the iterative calibration process of imaging devices  20  repeatedly over time, e.g., to maintain accuracy of calibration in mobile implementations of system  10 . In such embodiments, at each repetition, the first imaging device  20 A and the second imaging device  20 B may be synchronized by synchronization module  150  to produce respective images of the scene substantially at the same time. 
     Reference is now made to  FIG.  3 B , which is a block diagram depicting aspects of a system  10  for stereoscopic image analysis, according to some embodiments of the invention. System  10  of  FIG.  3 B  may be, or may include the same system  10  as depicted in  FIG.  3 A . 
     According to some embodiments, system  10  may include a 3D analysis module  170 , configured to triangulate between one or more pixels depicted in image  20 A′ and one or more corresponding pixels depicted in image  20 B′. 3D analysis module  170  may perform this triangulation based on optical flow  110 A (from optical flow module  110 ), e.g., based location of the one or more pixels in image  20 A′, in relation to location of one or more corresponding pixels in image  20 B′. Additionally, 3D analysis module  170  may perform the triangulation further based on, or considering the at least one parameter of relative position  120 A (from calibration module  120 ). 
     3D analysis module  170  may thus obtain 3D coordinates  170 A of one or more respective points in the scene, based on the process of triangulation, as known in the art. Additionally, or alternatively, 3D analysis module  170  may obtain 3D coordinates  170 A of a sufficient number of points in the depicted scene, so as to produce a 3D representation  170 A′ of at least a portion the scene. In other words, the calculated 3D coordinates  170 A may comprise or constitute a 3D representation  170 A′ of the scene, as shown in  FIG.  4 D  (depicting a depth ground truth image of the scene of  FIGS.  4 A and  4 B ) and  FIG.  4 E  (depicting a calculated 3D representation  170 A′ that may be generated according to embodiments of the invention). 
     For example, calculated 3D representation  170 A′ may be, or may include a data structure (e.g., a tensor or matrix) that may represent a depth map, a disparity map, a point cloud and the like, as known in the art. 
     According to some embodiments, 3D representation  170 A′ may be associated with one or more values of confidence level  120 C. For example 3D representation  170 A′ may be a point cloud, in which one or more (e.g., each) point may represent a spatial point (e.g., having X, Y and Z coefficients) in the depicted scene, and wherein one or more (e.g., each) point may be associated, or attributed a confidence level  120 C. 
     In some embodiments, 3D representation  170 A′ may be presented (e.g., by output device  8  of  FIG.  1   , such as a monitor screen) according to confidence level  120 C. Pertaining to the point cloud example, each point may be associated with a color or brightness, in a scale that represents respective confidence levels  120 C. 
     Additionally, or alternatively, confidence level  120 C may be equal to, or may be calculated from convergence error  110 D. Pertaining to the example of  FIG.  6 A , confidence level  120 C may represent closeness of flow line  110 B to ROC  110 C, and may be calculated as an inverse value of convergence error  110 D. 
     In some embodiments, 3D analysis module  170  may determine 3D coordinates  170 A representing distances or depths of one or more points in the 3D scene, by performing triangulation on a pixel basis, considering the two cameras&#39; 20 different focal lengths and positions (e.g., relative position parameter values  120 A). During such triangulation, 3D analysis module  170  may consider pixels that correspond to the same 3D point in a scene (e.g., a first pixel from image  20 A′ and a corresponding pixel from image  20 B′) The projection lines of these pixels may intersect precisely at a point in the 3D scene that may be calculated from the coordinates of the two image points. 
     System  10  may employ 3D analysis module  170  to implement triangulation in a plurality of methods for matching stereoscopic images. Such methods may include, for example block matching and semi-global matching, as known in the art. 
     Additionally, or alternatively, system  10  may include an object detection module  160 , configured to detect at least one object  160 A (e.g., a person, a car, a motorcycle, etc.) from at least one image (e.g.,  20 A′,  20 B′). For example, object detection module  160  may be, or may include a machine-learning (ML) based model, configured to identify, segment and/or otherwise represent an object  160 A depicted in at least one image (e.g.,  20 A′,  20 B′), as known in the art. 
     It may be appreciated that the same object may look different or slightly different in any of the images taken from respective points of view of cameras  20 . However, based on parameters  120 A of the plurality of cameras  20 , as provided by calibration module  120 , 3D analysis module  170  may easily produce a 3D representation  170 A′ of an object  160 A included in the depicted scene. 
     Reference is now made to  FIG.  3 C , which is a block diagram depicting aspects of a system  10  for stereoscopic image analysis, according to some embodiments of the invention. System  10  of  FIG.  3 C  may be, or may include the same system  10  as depicted in  FIG.  3 A  and/or  FIG.  3 B . 
     As shown in  FIG.  3 C , system  10  may include a rectification module  180 , configured to analyze at least one of image  20 A′ and image  20 B′ to produce, based on the plurality of flow lines  110 B, a respective plurality of straight epipolar lines  180 B having a common origin point  180 C. The common origin point  180 C may correspond to, or be derived from the region of convergence  110 C in image  20 A′ after the process of rectification, and may be referred to herein as an Epipole  180 C. 
     System  10  may be employed to process highly distorted images  20 ′, e.g., images acquired by cameras  20  characterized by intrinsic parameters  120 B such as a short focal length, a wide field of view, and/or any other type of optical distortion. It has been experimentally observed that when images  20 A′ and/or image  20 B′ include such distortion, flow lines  110 B may not converge into a required ROC  110 C. 
     According to some embodiments, rectification module  180  may analyze or process images  20 A′ and/or  20 B′ by applying an image rectification function. In other words, rectification module  180  may rectify images  20 A′ and/or  20 B′, to produce respective first rectified image  180 -RECA and second rectified image  180 -RECB, as known in the art. 
     It may be appreciated that rectified images  180 -RECA and  180 -RECB may be characterized by having similar camera parameters (e.g., FOV, aspect ratio, resolution, etc.) and/or similar orientation (e.g., in the pitch, yaw and/or roll axes). Additionally, or alternatively, rectified images  180 -RECA and  180 -RECB may be characterized by having a minimal level of image distortion. Therefore, images  180 -RECA and  180 -RECB may include alignment of flow lines  110 B (e.g., as depicted in  FIGS.  6 A and  6 B  as part of first image  20 A′) into straight epipolar lines  180 B, that intersect at the common origin point  180 C in a plain of the first rectified image  180 -RECA. 
     In other words, epipolar lines  180 B may correspond to flow lines  110 B of image  20 A′ in a sense that epipolar lines  180 B may be derived from of flow lines  110 B during rectification of images  20 A′ and  20 B′ (e.g., during creation of rectified images  180 -RECA and  180 -RECB). 
     Additionally, or alternatively, the common origin point (e.g., epipole  180 C) may correspond to region of convergence  110 C in a sense that epipolar lines  180 B may converge into within a minimal area in rectified image  180 -RECA, the center of which represents the predefined intersection point of flow lines  110 B in image  20 A′, following image rectification. 
     As elaborated herein (e.g., in relation to  FIG.  3 A ), each flow line  110 B may represent an optical flow between a pixel of a first image  20 A′ and a corresponding pixel of the second image  20 B′. Therefore, each epipolar line  180 B may be viewed as representing an optical flow between a pixel of the first rectified image  180 -RECA and a corresponding pixel of the second rectified image  180 -RECB. 
     As shown in  FIG.  3 C , system  10  may include a fast matching module  130  (or “module  130 ” for short). As elaborated herein, module  130  may be configured to match one at least one pixel, patch or region in rectified image  180 -RECA with at least one corresponding pixel, patch or region in rectified image  180 -RECB by exploiting the benefit of epipolar geometry as elaborated herein (e.g., in relation to  FIG.  2   ). The term “fast” may be used in this context to indicate that module  130  may map between corresponding regions in images  20 A′ and  20 B′ (or between  180 -RECA and  180 -RECB) by limiting a search for compatible or corresponding pixels along epipolar lines  180 B, based on the constraints of epipolar geometry, as explained herein. 
     Given a location of one or more first pixels in image  180 -RECA (e.g., rectified version of image  20 A′), module  130  may limit a search for the one or more corresponding pixels in image  180 -RECB (e.g., rectified version of image  20 B′) along an epipolar line  180 B of the plurality of epipolar lines. 
     In other words, module  130  may be configured to select a first pixel in the first rectified image  180 -RECA, and identify an epipolar line  180 B that connects that pixel with the common origin point  180 C in the first rectified image, e.g., as depicted in the example of  FIG.  6 B . Module  130  may then identify a subset of pixels in the second rectified image  180 -RECB, that pertain to, or are defined by a location of the determined epipolar line in the first rectified image. For example, the selected subset of pixels in second rectified image  180 -RECB may correspond to, or depict the same portions of the scene, as the pixels along epipolar line  180 B in first rectified image  180 -RECA. In another example, the selected subset of pixels in second rectified image  180 -RECB may correspond to pixels that are defined by an area that surrounds the same portions of the scene as the pixels along epipolar line  180 B in first rectified image  180 -RECA, within a predetermined distance or radius. 
     Module  130  may then select a second pixel among the subset of pixels as matching the first pixel of the first rectified image. Module  130  may perform this selection based on a predetermined similarity metric. 
     For example, module  130  may select the matching pixel as the one most similar in color, or brightness to that of the first pixel. Additionally, or alternatively, module  130  may select the matching pixel based on regional, or morphological features, in a window surrounding the member pixels of the subset of  180 -RECB pixels. 
     Additionally, or alternatively, matching module  130  may perform the matching process by applying a transformation of coordinates on at least one of rectified images  180 -RECA and  180 -RECB, e.g., from cartesian coordinates to polar coordinates. In such embodiments, matching module  130  may represent image  180 -RECA with polar coordinates, having the epipole of camera  20 B as the origin point of these polar coordinates, and then easily finding the corresponding pixel in image  20 B′ by using the polar coordinates&#39; representation of image  20 A′. Following the epipolar geometry based, fast matching process of module  130 , 3D analysis module  170  may apply a depth estimation algorithm (e.g., triangulation) to one or more pairs of points depicted by images  180 -RECA and  180 -RECB, thereby determining distance of one or more points in the depicted scene, as elaborated herein. 3D analysis module  170  may perform such depth estimation by triangulating pairs of corresponding pixels, while considering the two cameras&#39; 20 different focal lengths and positions. Additionally, or alternatively, 3D analysis module  170  may produce a 3D representation  170 A (e.g., a depth map) of the captured scene, as elaborated herein. 
     As elaborated herein, system  10  may include any combination of two or more cameras  20  or imaging devices  20  to capture or take two or more pictures  20 ′ or images of the same scene, area, zone, region or any other view. The two or more cameras  20  may be positioned or located such that each of the cameras may take a picture or image from a different point of view, e.g., different angle of view of the same scene. System  10  may receive the plurality of images  20 ′ from the two or more imaging devices  20 , and 3D analysis module  170  may subsequently be able to estimate a plurality of parameters, characteristics and/or features  170 B of a plurality of objects  160 A in the images. Such object parameters  170 B may include, for example a range of an object  160 A, a size of an object  160 A, a height of an object  160 A, breadth of an object  160 A, velocity of an object  160 A, location of an object  160 A, depth of an object  160 A and the like. 
     It may be appreciated that the same object  160 A may look different in any of the images taken from respective points of view of cameras  20 . However, based on the calibration and/or rectification of images  20 A′ and/or  20 B′ as elaborated herein, matching module  130  may easily match between a first object  160 A, as captured by a first image (e.g.,  20 A′), and an expected location of the depicted object in the complementary image (e.g.,  20 B′), by restricting the search for the matching object to a specific area, e.g., along or around pixels of epipolar lines  180 B, as elaborated herein. 
     According to some embodiments, object detection module  160  may apply an object-detection algorithm on the first rectified image  180 -RECA to identify an object  160 A depicted in the first image  20 A′. Fast matching module  130  may then match the detected object  160 A in the first image with a corresponding area or object in the second rectified image  180 -RECB, by allowing object detection module  160  to search the corresponding object along epipolar line  180 B of the plurality of epipolar lines  180 B. Thus, system  10  may exploit the constraints of epipolar geometry to reduce the computational load of the search process, and expediate detection of objects  160 A in image  20 B′. 
     As elaborated herein, embodiments of the invention may include a stereoscopic imaging system  10  that may include two, possibly synchronized, imaging devices or sensors  20  (e.g., elements  20 A,  20 B), that may have parallel imaging planes. 
     Rectification in such a system may include image transformation for each of the sensors  20  (e.g.,  20 A,  20 B), such that their images (e.g.,  20 A′,  20 B′ respectively) are warped to simulate images (e.g., denoted herein as images  180 -RECA,  180 -RECB, respectively) produced by virtual sensors. These virtual sensors may be located at the same place as sensors  20 , and may be rotated to a mutual orientation. The mutual orientation may not necessarily be perpendicular to a translation vector, e.g., the average orientation between the sensors. Such rectification means that in the rectified images  180 -RECA,  180 -RECB epipolar lines  180 B may converge at a single point of convergence, epipole  180 C, on the image planes, which may have finite coordinates. 
     Calibrating system  10  may be done by finding an optical flow  110 A between two sensor images  20 ′ (e.g.,  20 A′,  20 B′) and finding calibration parameters  120 A/ 120 B that cause flow lines  110 B to converge. In other words, using the calibration  120  to rectify the optical flow coordinates (e.g., by creating rectified images  180 -RECA,  180 -RECB) will create flow-lines  110 B (e.g., now epipolar lines  180 B) that converge at a single point of convergence (e.g., epipole  180 C). 
     In other words, using calibration  120 , an optical flow  110 A may be fit into an epipolar constraint. Alternatively, calibration  120  may be used for an epipolar constraint on an optical flow being estimated. The optical flow output  110 A in these ways may be used for 3D reconstruction by triangulation. 
     When estimating an optical flow  110 A, the sensor images  20 ′ may be rectified with a calibration as a pre-process operation. This may reduce the complexity of the optical flow algorithm. 
     In stereo setups where same-plane rectification includes a large rotation between a sensor and its virtual counterpart a parallel-plane rectification may provide much less rotation, resulting in more detailed rectified images and retain more of the original sensors common field of view. These advantages may be used to estimate more accurate optical flow, calibration, and 3D reconstruction. 
     Reference is now made to  FIG.  7 A  and  FIG.  7 B , which illustrate similar stereoscopic imaging systems (denoted  900 A and  900 B), each mounting two synchronized imaging sensors from a bird eye view. 
       FIG.  7 A  illustrates a single plane rectification process, and  FIG.  7 B  illustrates a parallel-plane rectification process. 
     As shown in the example of  FIG.  7 A , system  900 A may include imaging sensors  20 A and  20 B, denoted herein as  900 A 2  and  900 A′ 2  respectively. The translation vector between sensors  900 A 2  and  900 A′ 2  is denoted  900 A 3 . In this example, translation vector  900 A 3  is not perpendicular to any of the sensors&#39; orientations. A rectification  180  of system  900 A may include warping the images of sensors  900 A 2  and  900 A′ 2  to simulate respective, virtual cameras  900 A 1  and  900 A′ 1 . Virtual cameras  900 A 1  and  900 A′ 1  may have similar intrinsic parameters  120 B as those of sensors  900 A 2  and  900 A′ 2 , may be devoid of distortion, and may be rotated so that their orientations are perpendicular to translation vector  900 A 3 . For example, the translation vector  900 A 3  may be horizontal in relation to the virtual cameras&#39; axes. 
     As shown in the example of  FIG.  7 B , system  900 B may include imaging sensors  20 A and  20 B, denoted herein as  900 B 2  and  900 B′ 2  respectively. The translation vector between sensors  900 B 2  and  900 B′ 2  is denoted  900 B 3 , and may not be perpendicular to any of the sensors&#39; orientation vectors. A rectification  180  of system  900 B may include warping of images of sensors  900 B 2  and  900 B′ 2  to simulate virtual cameras  900 B 1  and  900 B′ 1 , respectively. Virtual cameras  900 B 1  and  900 B′ 1  may have similar intrinsic parameters  120 B as those of sensors  900 B 2  and  900 B′ 2 , may be devoid of distortion, and may be rotated to a predefined direction, e.g. a front direction of system  900 B, and not substantially perpendicular to translation vector  900 B 3 . The virtual camera axes may not be perpendicular to translation vector  900 B 3 . 
     In other words, according to some embodiments, at least one of the first rectified image  180 -RECA and second rectified image  180 -RECA may represent a predefined direction of view (e.g., a front view of vehicle  200 ), that is not substantially perpendicular to (e.g., removed by at least a predefined angle from) translation vector  120 A/ 900 A 3  which defines the translation between the first imaging device  20 A/ 900 B 2  and second imaging device  20 B/ 900 B′ 2 . 
     It may be appreciated that the parallel-plane rectification process shown in  FIG.  7 B  includes less virtual rotation than the single plane rectification depicted in  FIG.  7 A . In other words, there is less rotation between the original cameras and their virtual counterparts in  FIG.  7 B , in relation to  FIG.  7 A . Due to this reduced virtual rotation, there is also higher overlap between a camera&#39;s FOV and that of it&#39;s virtual counterpart in  FIG.  7 B  in relation to  FIG.  7 A . Subsequently, there is also reduced loss of data in the image rectification process depicted in  FIG.  7 B , in relation to that of  FIG.  7 A . 
     Additionally, due to geometric reasons, epipolar lines  180 B in parallel-plane rectified images (e.g., as presented in  FIG.  7 B ) may converge at a center of convergence having finite coordinates. In comparison, epipolar lines in a same-plane rectified images (e.g., as presented in  FIG.  7 A ) may all be horizontal (e.g., converge in infinity). 
     Reference is now made to  FIG.  8 A , which is a flowchart depicting a method of image analysis by at least one processor (e.g., processor  2  of  FIG.  1   ) as elaborated herein, according to some embodiments of the invention. 
     As shown in steps S 1005  and S 1010 , the at least one processor  2  may receive, from a first imaging device or camera  20 A, having a first FOV, and located at a first, initially unknown position, a first image  20 A′ of a scene, and receive, from a second imaging device  20 B, having a second, different FOV, and located at a second, initially unknown position, a second image  20 B′ of the scene. 
     As shown in step S 1015 , the at least one processor  2  may calculate a plurality of flow lines  110 B in a plane of the first image, wherein each flow line  110 B may represent an optical flow  110 A between a pixel of the first image  20 A′ and a corresponding pixel of the second image  20 B′. 
     As shown in step S 1020 , the at least one processor  2  may calibrate the imaging devices by determining at least one parameter of relative position  120 A between the first imaging device  20 A and second imaging device  20 B, based on the calculated flow lines  110 B, as elaborated herein (e.g., in relation to  FIG.  3 A ). 
     Reference is now made to  FIG.  8 B , which is a flowchart depicting another method of image analysis by at least one processor  2  as elaborated herein, according to some embodiments of the invention. 
     As shown in steps S 2005  and S 2010 , the at least one processor  2  may receive, from a first imaging device or camera  20 A, having a first FOV, and located at a first, initially unknown position, a first image  20 A′ of a scene, and receive, from a second imaging device  20 B, having a second, different FOV, and located at a second, initially unknown position, a second image  20 B′ of the scene. 
     As shown in step S 2015 , the at least one processor  2  may calibrate at least one of the first imaging device and second imaging device as elaborated herein (e.g., in relation to  FIGS.  3 A,  3 C,  6 A and  6 B ), to obtain an origin point or epipolar point  180 C in a plane of the first image  20 A′. The origin point or epipolar point  180 C may define convergence of a plurality of epipolar lines  180 B in a plane of the first image  20 A′ (or  180 -RECA). As elaborated herein (e.g., in relation to  FIG.  3 C ), each epipolar line  180 B may represent an optical flow  110 A between the first image  20 A′ (or  180 -RECA) and the second image  20 B′ (or  180 -RECB). 
     As shown in step S 2015 , the at least one processor  2  may match one or more pixels in the first image  20 A′ (or  180 -RECA) with one or more corresponding pixels in the second image  20 B′ (or  180 -RECA) by searching the one or more corresponding pixels along an epipolar line  180 B of the plurality of epipolar lines  180 B. 
     Reference is now made to  FIG.  8 C , which is a flowchart depicting a method of image analysis by at least one processor (e.g., processor  2  of  FIG.  1   ) as elaborated herein, according to some embodiments of the invention. 
     As shown in steps S 3005  and S 3010 , the at least one processor  2  may receive, from a first imaging device or camera  20 A, having a first FOV, and located at a first, initially unknown position, a first image  20 A′ of a scene, and receive, from a second imaging device  20 B, having a second, different FOV, and located at a second, initially unknown position, a second image  20 B′ of the scene. 
     As shown in step S 3015 , the at least one processor  2  may receive (e.g., via input device  7  of  FIG.  1   ) a set of calibration parameters, including for example parameters of relative position  120 A between the first imaging device (e.g.,  20 A) and second imaging device (e.g.,  20 B), and/or intrinsic camera parameters  120 B. 
     As shown in step S 3020 , and as elaborated herein (e.g., in relation to  FIG.  3 C ), the at least one processor  2  (e.g., rectification module  180 ) may apply a rectification function on images  20 A′ and  20 B′, to create two respective, rectified images of the scene ( 180 -RECA and  180 -RECB), so as to produce epipolar lines  180 B that are concentric, and in known locations and orientations at least one of images  20 A′ and  20 B′. For example, images  180 -RECA and  180 -RECB may be set so as to dictate an epipole  180 C of epipolar lines  180 B in a predefined position in a plane of at least one of images  180 -RECA and  180 -RECB. 
     As shown in step S 3025 , and as elaborated herein (e.g., in relation to  FIG.  3 C ), the at least one processor  2  (e.g., fast matching module  130 ) may match one or more pixels in the first image  20 A′ (or  180 -RECA) with one or more corresponding pixels in the second image  20 B′ (or  180 -RECB, respectively) by searching the one or more corresponding pixels along an epipolar line  180 B of the plurality of epipolar lines, thus implementing an optimized search algorithm. 
     Embodiments of the invention provide a practical application in the technological field of stereoscopic imaging. As elaborated herein, Embodiments of the invention may provide several improvements in computational image analysis, in relation to currently available systems and methods of camera calibration, image matching, distance assessment and 3D image processing. 
     Unless explicitly stated, the method embodiments described herein are not constrained to a particular order or sequence. Furthermore, all formulas described herein are intended as examples only and other or different formulas may be used. Additionally, some of the described method embodiments or elements thereof may occur or be performed at the same point in time. 
     While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents may occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention. 
     Various embodiments have been presented. Each of these embodiments may of course include features from other embodiments presented, and embodiments not specifically described may include various features described herein.