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An efficient unambiguous stereo matching technique is presented in this paper. Our main contribution is to introduce a new reliability measure to dynamic programming approaches in general. For stereo vision application, the reliability of a proposed match on a scanline is defined as the cost difference between the globally best disparity assignment that includes the match and the globally best assignment that does not include the match. A reliability-based dynamic programming algorithm is derived accordingly, which can selectively assign disparities to pixels when the corresponding reliabilities exceed a given threshold. The experimental results show that the new approach can produce dense (>70 percent of the unoccluded pixels) and reliable (error rate Index Terms--Stereo, dynamic programming.

First Sight, a vision system in labeling the outline of a moving human body, is proposed in this paper. The emphasis of First Sight is on the analysis of motion information gathered solely from the outline of a moving human object. Two main processes are implemented in First Sight. The first process uses a novel technique to extract the outline of a moving human body from an image sequence. The second process, which employs a new human body model, interprets the outline and produces a labeled two-dimensional human body stick figure for each frame of the image sequence. Extensive knowledge of the structure, shape, and posture of the human body is used in the model. The experimental results of applying the technique on unedited image sequences with self-occlusions and missing boundary lines are encouraging. Index Items - Coincidence edge, difference picture, human body, human body model, labeling, model, motion, outline, pose, posture, ribbon, stick figure.

Image segmentation is the process by which an original image is partitioned into some homogeneous regions. In this paper, a novel multiresolution color image segmentation (MCIS) algorithm which uses Markov Random Fields (MRF's) is proposed. The proposed approach is a relaxation process that converges to the MAP (maximum a posteriori) estimate of the segmentation. The quadtree structure is used to implement the multiresolution framework, and the simulated annealing technique is employed to control the splitting and merging of nodes so as to minimize an energy function and therefore, maximize the MAP estimate. The multiresolution scheme enables the use of different dissimilarity measures at different resolution levels. Consequently, the proposed algorithm is noise resistant. Since the global clustering information of the image is required in the proposed approach, the scale space filter (SSF) is employed as the first step. The multiresolution approach is used to refine the segmentation. Experimental results of both the synthesized and real images are very encouraging. In order to evaluate experimental results of both synthesized images and real images quantitatively, a new evaluation criterion is proposed and developed.

In the early 1980s, Pentland observed that the human's eye is sensitive to the change of intensities. On an image of a smooth surface, the change of intensities is maximal whenever the illuminant direction is perpendicular to the normal of the surface. This motivates us to introduce the concept of critical points, where the surface normal is perpendicular to some light source direction. Apparently, the illuminant direction has a simple geometric relationship with the corresponding critical points. In this paper, for simplicity reasons, we restrict our discussions to the shading of a Lambertian sphere of known size in a multiple distant light source environment. A novel global representation of the intensity function is derived. Based on this intensity characterization, the least-squares and iteration techniques are used to determine critical points and, thus, the light source directions and their intensities if certain conditions are satisfied. The performance of this new approach is evaluated using both synthetic images and real images. As an application, we use it as a tool to determine light sources in real image synthesis. The experimental results show that this technique can be used to superimpose synthetic objects with a real scene. Index Terms--Computer vision, critical point, illuminant direction detection, image synthesis.

Match cost functions are common elements of every stereopsis algorithm that are used to provide a dissimilarity measure between pixels in different images. Global stereopsis algorithms incorporate assumptions about the smoothness of the resulting distance map that can interact with match cost functions in unpredictable ways. In this paper, we present a large-scale study on the relative performance of a structured set of match cost functions within several global stereopsis frameworks. We compare 272 match cost functions that are built from component parts in the context of four global stereopsis frameworks with a data set consisting of 57 stereo image pairs at three different variances of synthetic sensor noise. From our analysis, we infer a set of general rules that can be used to guide derivation of match cost functions for use in global stereopsis algorithms.