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Proceedings Paper

Recovering absolute depth and motion of multiple objects from intensity images
Author(s): Fan Jiang; Brian G. Schunck
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Paper Abstract

This paper reports an algorithm that recovers absolute depth and motion of multiple objects from intensity images. It has been shown that absolute depth of multiple objects can be recovered from relative normal flows. With normal flows and depth estimated from images, motion recovery becomes a linear regression problem. As the depth estimates can be very noisy, the method of least-median of squares (LMS) is used to robustly recover the object motion. To decompose image points into groups that correspond to independently moving objects, strong-edge points at which normal flows are estimated are grouped into segments. The segments are then grouped into rigidly moving objects if they can be interpreted as such. This algorithm is designed for the case of general motion. However, experiments suggest that rotation makes the estimation of quality normal flows difficult and brings large errors into depth and motion recovery. The algorithm is tested on real images of translating objects.

Paper Details

Date Published: 1 October 1991
PDF: 12 pages
Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); doi: 10.1117/12.48376
Show Author Affiliations
Fan Jiang, Univ. of Michigan (United States)
Brian G. Schunck, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 1569:
Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision
Su-Shing Chen, Editor(s)

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