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Proceedings Paper

Dynamic Motion Vision
Author(s): Joachim Heel
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Paper Abstract

We present a method for the recovery of environment structure and camera motion from a sequence of images taken by a camera in motion. Unlike previous approaches, our method of Dynamic Motion Vision explicitly models the perceived temporal variation of the scene structure in the form of a dynamical system. We use the Kalman Filter algorithm to optimally estimate depth values at every picture cell from optical flow. We interleave a least-squares motion estimation with the stages of the Kalman Filter. Our algorithm can therefore estimate both the structure of a scene and the camera motion simultaneously in an incremental fashion which improves the estimates as new images become available. Results of experiments on synthetic and real images are presented.

Paper Details

Date Published: 1 March 1990
PDF: 12 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969789
Show Author Affiliations
Joachim Heel, Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

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