The problem of segmenting an image into separate regions and tracking them over time is one of the most significant problems in vision. Terzopoulos et al. (1987) proposed an approach to detect the contour regions of complex shapes, assuming a user selected initial contour not very far from the desired solution. We propose to further explore the information provided by the user's selected points and apply an optimal method to detect contours which allows a segmentation of the image. The method is based on dynamic programming (DP), and applies to a wide variety of shapes. It is exact and not iterative. We also consider a multiscale approach capable of speeding up the algorithm by a factor of 20, although at the expense of losing the guaranteed optimality characteristic. The problem of tracking and matching these contours is addressed. For tracking, the final contour obtained at one frame is sampled and used as initial points for the next frame. Then, the same DP process is applied. For matching, a novel strategy is proposed where the solution is a smooth displacement field in which unmatched regions are allowed while cross vectors are not. The algorithm is again based on DP and the optimal solution is guaranteed. We have demonstrated the algorithms on natural objects in a large spectrum of applications, including interactive segmentation and automatic tracking of the regions of interest in medical images.>

An optimal method to match elastic contours is proposed. The method is based on dynamic programming and on some ideas borrowed from stereo matching, where occlusions correspond to contour regions that have no match after the deformation. The matching between two circles of different sizes and centers is shown. One may consider this experiment as the matching between two images of an inflating balloon. The algorithm produces uniform matches along the perimeter despite the large difference in size of the two contours. The matching of the inner and outer walls of the heart left ventricle is also shown. This matching can give diagnostic information for the heart wall. Because of the restricted search space, the DP matching algorithm is very fast, matching two contours of 500 points in a few milliseconds.>

A Markov random field (MRF) formulation for the problem of optical flow computation is studied. An adaptive window matching scheme is used to obtain a good measure of the correlation between the two images. A confidence measure for each match is also used. Thus, the input to the system is the adaptive correlation and the corresponding confidence. The MRF model is then used to estimate the velocity field and the velocity discontinuities. The problem of occlusions is addressed, and a relationship between occlusions and motion discontinuities is established.>

A hierarchical system for character recognition with hidden Markov model knowledge sources which solve both the context sensitivity problem and the character instantiation problem is presented. The system achieves 97-99% accuracy using a two-level architecture and has been implemented using a systolic array, thus permitting real-time (1 ms per character) multifont and multisize printed character recognition as well as handwriting recognition.

The design of a morphological processing system is presented, to be used in medical image enhancement and compression. The system consists of a gray scale dilation/erosion systolic array capable of video data rates. The architecture can be implemented with either one dimensional or two dimensional building blocks that accept raster scanned data and exhibits low latency and an optimal pipeline rate. Two processing element designs are presented, one based on programmable gate arrays and one suitable for standard cell VLSI implementation.>

The authors present a hierarchical system for character recognition with hidden Markov model knowledge sources that solve both the context sensitivity problem and the character instantiation problem. The system achieves 97 to 99% accuracy using a two-level architecture and has been implemented using a systolic array, thus permitting real-time (1 ms per character) multifont and multisize printed character recognition as well as handwriting recognition.>