Abstract:
A method of extracting a moving object boundary includes estimating an initial motion vector for an object whose motion is represented by a change in position between a target image and a reference image, estimating an initial vector for a background area over which the object appears to move, using the estimated vectors to find a first iteration of a dynamical model solution, and completing at least one subsequent iteration of the dynamical model solution so as to extract a boundary of the object.

Description:
FIELD OF INVENTION  
       [0001]     The invention is related to the field of video compression.  
       BACKGROUND  
       [0002]     Moving object extraction methods are traditionally used in video compression techniques to extract a contour of a moving object in a video sequence. Traditional methods often explicitly introduce a model to extract the contour. These traditional methods can have significant problems such as discretization of the contour and difficulty controlling the length and curvature as the contour evolves.  
         [0003]     For example, simple segmentation of a motion block can be performed to capture multiple moving objects so as to reduce the prediction error. This process can be achieved by using a quadtree segmentation of a block having a large prediction error into sub-blocks for improved motion estimation. The block having the large prediction error is typically quadtree segmented using a straight line model of the moving object&#39;s boundary.  
         [0004]     Other approaches in motion segmentation rely on optical flow estimates or parametric (i.e., affine) motion models. These approaches have problems, such as occlusion effects, near object boundaries. Some degree of smoothness in the segmentation field, and hence in object boundaries, can be achieved using MAP/Bayesian methods, which include a prior probability term. These methods constrain the connectivity of the segmentation field without any explicitly coupled model to account for the object boundary and motion fields.  
         [0005]     In some conventional approaches, a curvature evolution model is used to capture the moving object boundary. However, these approaches do not involve motion estimations, and they rely only on a temporal difference operator in the model for object boundary evolution.  
         [0006]     There is a need for a moving object extraction method that performs a region competition so as to grow the object from an initial condition, and to reach a state that provides a balance among prediction error reduction, boundary stability (i.e., no holes in the object, and a smoothness to the contour), and a coupling to image features.  
       SUMMARY  
       [0007]     A method of extracting a moving object boundary includes estimating an initial motion vector for an object whose motion is represented by a change in position between a target image and a reference image, estimating an initial vector for a background area over which the object appears to move, using the estimated vectors to find a first iteration of a dynamical model solution, and completing at least one subsequent iteration of the dynamical model solution so as to extract a boundary of the object.  
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0008]     The present invention is illustrated by way of example and may be better understood by referring to the following description in conjunction with the accompanying drawings, in which:  
         [0009]      FIG. 1  shows an example of a method of extracting a moving object boundary from a background region.  
         [0010]      FIG. 2  shows an example of a boundary field that is grown with the dynamical model.  
         [0011]      FIG. 3A  shows examples of occlusions that can occur when using one reference image with the dynamical model, and  FIGS. 3B, 3C  and  3 D show an example of avoiding an occlusion when using multiple reference images with the dynamical model.  
         [0012]      FIG. 4  shows an example of a method for generating a hypothesis for the background motion field.  
         [0013]      FIG. 5  shows an example of generating a hypothesis for the object motion field.  
         [0014]      FIG. 6  shows an example of a portion of a boundary field and normal vector.  
         [0015]      FIG. 7  shows an example of the effects of the image coupling driving term.  
         [0016]      FIG. 8  shows an example of an emerging boundary field for an object and illustrates local boundary motion vectors.  
         [0017]      FIG. 9  shows an example of video coding for encoding an image (or frame, or field) of video data that uses the method of extracting a moving object  
         [0018]      FIG. 10  shows an example of decoding a image (or frame, or image) of video data.  
         [0019]      FIG. 11  shows an example of a system that uses the method of extracting a moving object boundary.  
     
    
     DETAILED DESCRIPTION  
       [0020]     In the following description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration a specific embodiment in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present invention. For example, skilled artisans will understand that the terms field or frame or image that are used to describe the various embodiments are generally interchangeable as used with reference to video data.  
         [0021]     An object extraction method estimates the contours of a moving foreground object represented in video images by using a dynamical model to evolve the boundary of the object. The dynamical model allows compact and coherent structures to emerge. In some embodiments, the dynamical model uses a two-dimensional boundary field, which is defined at each pixel in the image so that no constrained or parameterized boundary model is needed, to extract the object boundary. The dynamical model also uses a local object motion field to provide motion vectors along the boundary that account for non-rigid motion of the object. The dynamical model incorporates a diffusion term and an annealing term to couple the boundary to image gradient features. The object extraction method performs a hypothesis testing of past and future prediction errors so as to minimize errors caused by occlusion. The method allows object motion vectors at the boundary of the object to handle more complex local object motion. The method can be used in motion segmentation or video coding applications to generate motion vector sampling for improved temporal prediction of a target image.  
         [0022]     An example of a method of extracting a moving object boundary from a background region extracts a contour of a single object from the background is shown in  FIG. 1 . At  110 , a dynamical model is defined. At  120 , an initial seed for the boundary field is placed in a region of the target image. At  125  initial values of motion vectors are determined. At  130 , a state of the dynamical model is advanced by a time step to evolve the boundary field. At  140 , motion vectors representing the background and the moving object are re-estimated. At  150 , the method determines whether a stopping criterion has been reached. If so, the method ends at  160 . Otherwise, the method returns to  130 .  
         [0000]     Dynamical Model for the Boundary Field  
         [0023]     A dynamical model of a two-dimensional boundary field B(x, y) is defined, where B is a value for a pixel at location (x, y) in the image. A positive value of B indicates that the corresponding pixel is within an object, and a negative value indicates that the pixel is in the background. The method starts with an initial condition for B(x, y) and iteratively evolves the boundary field to form a better estimate of the object boundary.  
         [0024]     In some embodiments, the method evolves the boundary field by numerically solving the dynamical model and advancing it forward in time. The boundary field is then expressed as a function of time, B(x, y, t), where the initial condition B o (x,y) starts at an initial time of 0, such that 
 
 B   o ( x, y )= B ( x, y, t= 0). 
 
 The initial condition is 
 
B o (x,y)˜1 
 
 for a region within an object, and 
 
B o (x,y)˜−1 
 
 elsewhere, with a gradual transition between the two states. The initial condition may also be seeded with prior knowledge about the object boundary to improve performance, if such knowledge is available. In some embodiments, the initial value for the boundary field is:  
           B   o     ⁡     (     x   .   y     )       =       -     1   2       +     exp   (     -     a   ⁡     (         (     x   -     x   o       )     2     +       (     y   -     y   0       )     2       )                 
 
 where (x o , y o ) is the center of the seed, and a measures the size of the seed. 
 
         [0025]     The method grows the seed around a gradient V of the boundary field B(x, y), where B(x, y)˜0, using an evolution equation: 
 
∂ t   B ( x,y,t )= T|∇B ( x,y )|  (1) 
 
 to evolve the dynamical model according to: 
 
 B ( x,y,t+ τ)= B ( x,y,t )+τ T|∇B ( x,y,t )|  (2) 
 
 where T is a composite driving term, and τ is a time step parameter to advance the state from time t to t+τ. The method is repeated until a stopping criterion, such as convergence, is reached. 
 
         [0026]     An example of boundary fields that are grown with the dynamical model is shown in  FIG. 2 . A target image is shown in block  205 . To determine a boundary of object  202  in the target image  205 , a seed  212  is placed in the target image, as shown in block  210 . To determine a boundary of object  204 , a seed  214  is placed in the target image, as shown in block  210 . The boundary of seed  212  is evolved, as shown in blocks  220  and  230 , to form an object region represented by the white area  222 . Similarly, the boundary of seed  214  is evolved, to form an object region represented by the gray area  224 . As shown in block  230 , the total gray object region  234  that is determined from seed  214  has smaller portions that are not connected to the larger portion, as well as background regions within the larger portion. Similarly, the total object region  232  represented by the white area has some disconnected portions. A stability driving term, discussed below, is used to control the disconnected regions. The boundary  242  of the entire object  202  is captured, as shown in block  240 . Similarly, the boundary of seed  214  is evolved until the boundary  244  of the entire object  204  is captured.  
         [0027]     The composite driving term T in eq. (2) is a combination of terms, such as the prediction error, stability, coupling, and template driving terms:  
             T   =         λ   1     ⁢     T       past   /   future       prediction   ⁢   _   ⁢   error           +       λ   2     ⁢     T   stability       +       λ   3     ⁢     T     image_couplin   ⁢   g         +       λ   4     ⁢     T   template                 (   3   )             
 
 Each weighting value {λ i } determines the relative strength of the corresponding driving term. The past/future prediction error driving term includes the error from past and future reference image processing. This driving term is considered during a hypothesis testing method to account for occlusions and uncovered regions, as discussed below. The stability driving term T stability  is used to ensure that B(x, y) maintains smoothness so that the extracted motion layers have a degree of compactness and the extracted moving object boundary has a degree of connectivity. The coupling driving term T image     —     coupling  is related to the spatial activity in the image. For example, an object can have correlation to local spatial activity, such as the correlation between an object boundary and an image gradient. The driving term T template  plate considers existing knowledge about a boundary in the image, if available. 
 
         [0028]     Including the expression of the driving terms from eq. (3) into the dynamical model from eq. (2) yields:  
                 ∂   t     ⁢           ⁢     B   ⁡     (     x   ,   y     )         =         (         λ   1     ⁢     T       past   /   future       prediction   ⁢   _   ⁢   error           +       λ   2     ⁢     T   stability       +       λ   3     ⁢     T     image_couplin   ⁢   g         +       λ   4     ⁢     T   template         )     ⁢          ∇     B   ⁡     (     x   ,   y     )                =         ∂   t     ⁢       B   1     ⁡     (     x   ,   y     )         +       ∂   t     ⁢       B   2     ⁡     (     x   ,   y     )         +       ∂   t     ⁢       B   3     ⁡     (     x   ,   y     )         +       ∂   t     ⁢       B   4     ⁡     (     x   ,   y     )                     (   4   )             
 
 Past and Future Prediction Error Driving Term 
 
         [0029]     The past and future prediction error driving term represents the prediction error difference between using the background motion vector or the object motion vector at some pixel location. The estimate of the background motion vector is denoted as v b (x, y) and the object motion vector is denoted as v o (x, y). This driving term is expressed as:  
               T       past   /   future       prediction   ⁢   _   ⁢   error         =         ɛ   2     ⁡     (       v   b     ⁡     (     x   ,   y     )       )       -       ɛ   2     ⁡     (       v   o     ⁡     (     x   ,   y     )       )                 (   5   )             
 
 where ε 2 (v o ) is the prediction error at some pixel location when the motion vector v o (x, y) is used, and ε 2 (v b ) is the prediction error at the pixel location when the motion vector v b (x, y) is used. Placing this term in the dynamical model of eq. (3) yields the contribution of this term as: 
 
∂ t   B   1 ( x,y )=(ε 2 ( v   b )−ε 2 ( v   o ))|∇ B ( x,y )  (6) 
 
         [0030]     Thus, at pixel location (x, y), if the prediction error is smaller when the object motion vector is used (that is, (ε 2 (v b )−ε 2 (v o )) is positive), then B(x, y) increases since the time derivative is positive, and the pixel moves toward the object, which is expressed as pixel locations having positive values of B(x, y). Similarly, at pixel location (x, y), if the prediction error is smaller when the background motion vector is used (that is, (ε 2 (v b )−ε 2 (v o )) is negative), then B(x, y) decreases since the time derivative is negative, and the pixel moves toward the background, which is expressed as pixel locations having negative values of B(x, y).  
         [0031]     The prediction error increases if an occlusion is present in the reference image.  FIG. 3A  shows examples of occlusions produced by the boundary extraction method when only one reference image, either past or future, is used. In block  310 , the target image has an occlusion region  317 , which are pixels that are covered by object  315  in a past reference image but are not covered by the object  315  in the target image. As a result of this occlusion region that is dependent on a past reference image, there is a moving object boundary that has poor correspondence to the true object boundary near occlusion region  317  because one past reference field was used to iteratively solve the dynamical model when extracting the boundary of the moving object. To avoid producing the occlusion, the future reference image should be used, if available, to predict the pixels in the occlusion region because the pixels in occlusion region  317  are not covered by the object  315  in the future reference image. Similarly, in block  320  occlusion region  327  is a region that is not covered by object  315  in the target image but is covered by the object  315  in a future reference image. The result is a moving object boundary that has poor correspondence to the true object boundary near occlusion region  327  when only one future reference field is used to iteratively solve the dynamical model when extracting the boundary of the moving object. Therefore, the past reference image, if available, should be used to predict the pixels in occlusion region  327 .  
         [0032]     The occlusion regions shown in  FIG. 3A  can be avoided by using both past and future reference images when extracting the moving object boundary, as shown in  FIGS. 3B, 3C , and  3 D. In order to handle occlusion, past and future motion information is used. At any time in the iteration of the dynamical mode, the growth state of the object and the motion of object and background are used to determine which pixels should use past or future reference image for motion estimation. As shown in  FIG. 3B , future reference image  330  and past reference image  350  are used to extract the boundary of a moving object in target image  340 . As shown in  FIG. 3C , the dynamical model is initialized in the black region  341  of block  370  to produce a background region, represented by the black square, and a seed  342  within the boundary of the moving object. As shown in block  380 , the dynamical model is iteratively solved to extend the boundary of the moving object to produce moving object region  344 . The moving object has a motion vector  346  that shows the motion of the object is directed towards the right side of the target image. Therefore, to avoid producing an occluded region, the dynamical model uses the future reference image  330  to estimate motion vectors and prediction error for the region  381  to the left of dotted line  347 , and the model uses past reference image  350  to estimate motion vectors and prediction error for the region  382  to the right of dotted line  347 . In some embodiments, a determination to use either the past reference image or the future reference image is made at each pixel by performing a hypothesis test for background and object motion vectors, as explained below. The final result is shown in  FIG. 3D , as extracted boundary  349 , represented by the white lines around the object, which is produced without an occluded region and hence has better quality extraction.  
         [0033]     A more detailed pixel-wise decision on whether to use past or future reference field is made by extending the prediction error driving term as follows: 
 
∂ t   B   1 ( x,y )=(ε 2 ( v   b   hyp )−ε 2 ( v   o   hyp ))|∇ B ( x,y )|  (7) 
 
 where v hyp  denotes the hypothesis for the motion vector at pixel location (x,y). The hypothesis test for the background motion vector is performed as shown in  FIG. 4 . The motion vector for the object, using a past or a future reference image, is denoted as either: v o   past (x, y), or v o   future (x, y). The motion vector for the background, using a past or a future reference image, is denoted as either: v b   past (x, y), or v b   future (x, y). At  410 , if the motion vector v b   past  is consistent with a current object state and motion, and v b   future  is not consistent, then at  415  select v b   past . Otherwise, at  420 , if motion vector v b   past  is not consistent with the current object state and motion, and v b   fututr  is consistent, then at  425  select v b   future . Otherwise, if both motion vectors are consistent, then at  435  select the motion vector with the minimum prediction_error, 
 
 v   b   hyp ( x,y )=min v (ε 2 ( v=v   b   past ),ε 2 ( v=v   b   future )). 
 
         [0034]     The hypothesis test for the foreground object motion vector is performed as shown in  FIG. 5 . At  505 , a value for the boundary field B(x,y) is received. At  510 , determine if B(x, y)≧S. If so, then at  520 , select v o   hyp =min v (ε(v=v o   past ),ε(v=v o   future ). If not, then at  530 , select v o   hyp =max v (ε(v=v o   past ),ε(v=v o   future )). Thus, if the current state at pixel (x,y) has a value such that B(x, y)≧S, which means that the pixel is likely within the object, then the method uses v o   hyp =min v (ε(v=v o   past ),ε(v=v o   future ) to select a smaller prediction error for the object motion vector, which favors object growth. On the other hand, if the current state at pixel (x,y) has a value where B(x, y)&lt;S, which means that the pixel is more likely in the background, then the method uses v o   hyp =max v (ε(v=v o   past ),ε(v=v o   future ) to select a larger prediction error for the object motion vector in order to favor background growth.  
         [0035]     In this example, the parameter S is set it to 0, since object and background are separated by the zero values of B(x, y). The hypothesis selection of the object or background motion vector uses past and future reference image information, along with current object state information, to better handle the occlusion.  
         [0000]     Stability Driving Term  
         [0036]     The stability driving term allows for a compact, stable structure to emerge from the nonlinear model, and the term is expressed as: 
 
 T   stability   =−∇·{circumflex over (n)}   (8) 
 
 where {circumflex over (n)} is the normal vector for the boundary field, defined as:  
           n   ^     ⁡     (     x   .   y     )       =     -       ∇     B   ⁡     (     x   ,   y     )                ∇     B   ⁡     (     x   ,   y     )                      
 
 which is the direction normal to the curve where B(x, y)=constant. Placing this term in eq. (3) yields: 
 
∂ t   B   2 ( x,y )=−(∇· {circumflex over (n)})|∇   B ( x,y )  (9) 
 
 Thus, if the contour of the object near the boundary, where |∇B(x, y)| is nonzero, has a positive curvature (i.e., an outward shape from the positive region), then ∇·{circumflex over (n)} is positive, and B(x, y) decreases to straighten the curve. Similarly, if the contour of the object near the boundary, where |∇B(x, y)∇ is nonzero, has a negative curvature (i.e., an inward shape from the positive region), then ∇·{circumflex over (n)} is negative, and B(x, y) increases to straighten the curve. 
 
         [0037]     The stability driving term controls the degree of curvature of the object boundary topology. This term acts as a diffusion term that reduces the length of the contour.  FIG. 6  shows an example of a portion of a boundary field  610  and normal vector  620 . The stability driving term straightens the contour of the boundary field, as shown by dashed line  630 . An explicit diffusion term can also be added to eq. (9) to more directly control the removal (i.e., diffusion) of small positive or negative regions, as shown in eq. (10): 
 
∂ t   B   2 ( x,y )=−(∇− {circumflex over (n)} )|∇ B ( x, y )|−∇ 2   B ( x, y )  (10) 
 
 The Laplacian term on the right of eq. (10) causes the Boundary field to be relatively smooth and homogeneous. 
 
 Image Coupling Driving Term 
 
         [0038]     The moving object boundary may have a correlation to some local spatial image activity. For example, often an object boundary has an intensity gradient normal to the boundary. This type of local spatial activity correlation is incorporated into the model using the image coupling driving term: 
 
 T   image     —     coupling =∇·( {circumflex over (n)}|∇I ( x,y )|)  (11) 
 
 where {circumflex over (n)} is the normal to the boundary field, and |∇I(x, y)| is the magnitude of the image intensity gradient. Placing this term in eq. (3) yields the contribution of this factor as: 
 
∂ t   B   3 ( x,y )=(∇·( {circumflex over (n)}|∇I ( x,y )|))|∇ B ( x,y )|  (12) 
 
 Thus, if an image gradient is near the object boundary width, then the boundary aligns along the image gradient.  FIG. 7  shows an example of the effects of the image coupling driving term. A portion of a boundary curve  710  is close to an image structure  720 , such as a local peak in an intensity gradient. The image coupling driving term attracts the boundary to the image structure, as shown by curved line  730  and dashed line  740 . 
 
 Template Driving Term 
 
         [0039]     The template driving term is used, for instance, in an embodiment that learns information about the objects in the scene from previous sequences, or that has prior information about the expected shape of an object. This information provides a template for the object boundary. The object boundary template driving factor may be characterized by the crossing point of a two-dimensional function {tilde over (B)} template (x,y). The template driving term is expressed as: 
 
∂ t   B   4 ( x,y )=−( B ( x,y )− {tilde over (B)}   template ( x,y ))|∇ B ( x,y )|  (13) 
 
         [0040]     Thus, if the boundary field B(x, y) is larger than the template {tilde over (B)} template (x, y) at a pixel position near the object boundary, then B(x, y) decreases. Similarly, if the boundary field B(x, y) is smaller than the template {tilde over (B)} template (x, y) at a pixel position near the object boundary, then B(x,y) increases. Eventually, an equilibrium is reached where B(x, y)˜{tilde over (B)} template (x y).  
         [0041]     The dynamical model evolves the spatial two-dimensional boundary field B(x, y) according to eq. (4). The parameters {λ 1 , λ 2 , λ 3 } determine the relative weights of each term. In some embodiments, λ 3  is initially set to 0 and slowly increases so that it becomes more effective in later stages of the growth of the object boundary. Often, λ 4  is set to 0 for the entire method because no prior knowledge about the object boundary is available. The driving terms are functions of the boundary field B(x, y) and the motion field. The nonlinear cooperative effects of the driving terms allow for a stable evolution and emergence of the boundary field for the moving object.  
         [0042]     As the boundary field is updated in the dynamical model, the prediction error between using the background and object motion for each pixel “x” needs to be computed at every iteration. The background motion is usually very robust and stable, and so a single background motion may be used for all pixels. In some instances, however, the object motion may involve non-local or non-rigid motion. In these instances, bulk and local/boundary motion vectors for the object motion are used in some embodiments.  
         [0043]      FIG. 8  shows an example of an emerging boundary field for an object. At time t 1 , the identified portion of the object in target image  800  is shown at  810 , and the rest of the target image is considered background region. As the boundary field evolves at time t 2 , the identified portion of the object grows as shown at  820 . Boundary motion vectors  830  are placed along the boundary of the moving object, and values for the motion vectors are estimated. The values of the boundary motion vectors are determined using pixels that are identified as part of the object at the current time t 2 . That is, only pixels inside the identified portion of the object  820  are used to estimate the boundary motion vectors  830 . Each boundary motion vector is estimated using a region inside of the identified portion of the object, within a predetermined distance from the boundary. Each boundary motion vector therefore indicates motion of a small portion of the object. A bulk motion vector for the object, v bulk , is estimated using pixels from the identified portion to indicate the overall motion of the entire object.  
         [0044]     Generally, the bulk motion vector v bulk  can be used to represent the object motion for each pixel in the object. For a pixel along the boundary, a boundary motion vector that is near, or has local spatial correlation with, the pixel can be used to represent the object motion, in order to handle non-rigid motion in which several parts of the object move in different directions. For example, if an object is a person, a bulk motion vector can indicate that the person is moving to the right, and a boundary motion vector along a hand of the person can indicate that the hand is moving to the left relative to the bulk motion vector.  
         [0045]     In one embodiment, the boundary extraction method is used in video coding for encoding an image (or frame, or field) of video data, as shown in  FIG. 9 . At  910 , the encoder receives an input target image. A set of reference images, which contain decoded image data related to the target image, is available to the encoder during the encoding process, and also to the decoder during the decoding process. At  915 , moving object boundaries are extracted. At  920 , the encoder generates an irregular sampling, or distribution, of motion vectors associated with the target image. At  930 , the sampling pattern information (e.g., bits to represent the pattern) is transmitted to a decoder.  
         [0046]     At  940 , a temporal prediction filtering process is applied to the irregular motion sampling pattern. This adaptive filtering process uses the motion vectors, irregular sampling pattern, and reference images to generate a prediction of the target image. At  950 , the motion vector values are coded and sent to the decoder. At  960 , a residual is generated, which is the actual target data of the target image minus the prediction error from the adaptive filtering process. At  970 , the residual is coded and at  980  is sent to the decoder.  
         [0047]     In another embodiment, the adaptive sampling pattern of motion vectors is used in decoding a image (or frame, or image) of video data, as shown in  FIG. 10 . At  1010 , an encoded residual is received. At  1020 , the decoder decodes the received encoded residual. At  1030 , the decoder receives the sample pattern information, reference images, and motion vector values. Then, at  1040  the decoder applies the adaptive temporal filter procedure to generate the temporal prediction. At  1050 , the decoded target image is generated by adding the decoded residual to the temporal prediction.  
         [0048]      FIG. 11  shows an example of a system that uses the adaptive area of influence filter. A digital video camera  1110  captures images in an electronic form, and processes the images using compression device  1120 , which uses the motion vector selection method during the compression and encoding process. The encoded images are sent over an electronic transmission medium  1130  to digital playback device  1140 . The images are decoded by decoding device  1150 , which uses the filter during the decoding process. Camera  1110  is illustrative of various image processing apparatuses (e.g., other image capture devices, image editors, image processors, personal and commercial computing platforms, etc.) that include embodiments of the invention. Likewise, decoding device  1150  is illustrative of various devices that decode image data.  
         [0049]     While the invention is described in terms of embodiments in a specific system environment, those of ordinary skill in the art will recognize that the invention can be practiced, with modification, in other and different hardware and software environments within the spirit and scope of the appended claims.