Recovering absolute depth and motion of multiple objects from intensity images

Fan Jiang; Brian G. Schunck

doi:10.1117/12.48376

1 October 1991 Recovering absolute depth and motion of multiple objects from intensity images

Fan Jiang, Brian G. Schunck

Proceedings Volume 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision; (1991) https://doi.org/10.1117/12.48376
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

Fan Jiang and Brian G. Schunck "Recovering absolute depth and motion of multiple objects from intensity images", Proc. SPIE 1569, Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision, (1 October 1991); https://doi.org/10.1117/12.48376

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