Recursive computation of a wire-frame representation of a scene from dynamic stereo using belief functions

Arun P. Tirumalai; Brian G. Schunck; Ramesh C. Jain

doi:10.1117/12.48364

1 October 1991 Recursive computation of a wire-frame representation of a scene from dynamic stereo using belief functions

Arun P. Tirumalai, Brian G. Schunck, Ramesh C. Jain

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Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48364
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

This paper presents a stereo algorithm to recursively compute a boundary-level structural description of a scene, from a sequence of stereo images. The majority of existing stereo algorithms deal with individual points as the basic primitive to match between two or more images. While this keeps the implementation simple, the output description, which is a depth/disparity map, is represented as a composition of individual points. This is often undesirable as no semblance of the underlying structure of the scene is explicitly represented. A stereo matching algorithm is presented, based on connected line segments as the basic match primitive, which yields a description composed primarily of boundaries of objects in the scene. A description of this nature is very useful for obstacle avoidance and path planning for mobile robots. The stereo matching algorithm is integrated into a dynamic stereo vision system to compute and incrementally refine such a structural description recursively, using belief functions. The stereo camera motion between two viewpoints, which is necessary to register the two views, is recovered as part of the stereo computations. The approach is illustrated with a real dynamic stereo sequence acquired from a mobile robot.

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Arun P. Tirumalai, Brian G. Schunck, and Ramesh C. Jain "Recursive computation of a wire-frame representation of a scene from dynamic stereo using belief functions", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48364

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KEYWORDS

Image segmentation

Cameras

Image processing

Signal processing

Computer vision technology

Machine vision

Stochastic processes

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