A Bayesian Foundation for Active Stereo Visions

Larry Matthies; Masatoshi Okutomi

doi:10.1117/12.969965

1 March 1990 A Bayesian Foundation for Active Stereo Vision¹

Larry Matthies, Masatoshi Okutomi

Proceedings Volume 1198, Sensor Fusion II: Human and Machine Strategies; (1990) https://doi.org/10.1117/12.969965
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

Sensing three-dimensional shape is a central problem in the development of robot systems for autonomous navigation and manipulation. Stereo vision is an attractive approach to this problem in several applications; however, stereo algorithms still lack reliability and generality. We address these problems by modelling the stereo depth map as a discrete random field, by formulating the matching problem in terms of Bayesian estimation, and by using this framework to develop a "bootstrap" procedure that employs fine camera motion to initialize stereo fusion. First, one camera is translated parallel to the stereo baseline to acquire a narrow-baseline image pair; then, the depth map obtained from the narrow-baseline image pair is used to constrain matching in a "wide-baseline" image pair consisting of one image from each camera. The result of our procedure is an estimate of depth and depth uncertainty at each pixel in the image. This approach produces accurate depth maps reliably and efficiently, applies to indoor and outdoor domains, and extends naturally to multi-sensor systems. We demonstrate the potential of this approach by showing results c lined with scale models of difficult, outdoor scenes.

Citation Download Citation

Larry Matthies and Masatoshi Okutomi "A Bayesian Foundation for Active Stereo Vision¹", Proc. SPIE 1198, Sensor Fusion II: Human and Machine Strategies, (1 March 1990); https://doi.org/10.1117/12.969965

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