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Proceedings Paper

Computing image flow and scene depth: an estimation-theoretic fusion-based framework
Author(s): Ajit Singh
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Paper Abstract

Robust estimation of scene-depth is essential to many tasks in three dimensional visual perception. Image-flow is a major source of depth information. This paper decsribes a new framework for computing image-flow from time-varying imagery and recovering scene-depth from image-flow. In this framework, image-flow information available in the time-varying imagery is classified into two categories - conservation information and neighborhood information. Each type of information is recovered in the form of an estimate accompanied by a covariance matrix. Image-flow is then computed, along with confidence measures, by fusing the two estimates on the basis of their covariance matrices. The framework is shown to allow estimation of certain types of discontinuous flow-fields without any a- priori knowledge about the location of discontinuities. Furthermore, because of its estimation-theoretic nature, the framework lends itself naturally to incremental estimation of scene-depth using Kalman filtering-based techniques that fuse depth estimates over succesive frames of the image-seqeuence. The depth maps obtained by this scheme preserve the depth-discontinuities very well. This property of the framework is crucial to reliable recovery of 3-D features, e.g. depth-edges, from depth-maps. Algorithms based on this framework are used to recover image-flow and depth-maps from a variety of image-sequences.

Paper Details

Date Published: 1 April 1991
PDF: 19 pages
Proc. SPIE 1383, Sensor Fusion III: 3D Perception and Recognition, (1 April 1991); doi: 10.1117/12.25250
Show Author Affiliations
Ajit Singh, Siemens Corp. (United States)


Published in SPIE Proceedings Vol. 1383:
Sensor Fusion III: 3D Perception and Recognition
Paul S. Schenker, Editor(s)

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