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Proceedings Paper

Three-dimensional reconstruction from optical flow using temporal integration
Author(s): Ramesh M. Rangachar; Tsai Hong Hong; Martin Herman; Randall L. Luck
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Paper Abstract

Image flow the apparent motion of brightness patterns on the image plane can provide important visual information such as distance shape surface orientation and boundaries. It can be determined by either feature tracking or spatio-temporal analysis. The optical flow thus determined can be used to reconstruct the 3-D scene by determining the depth from camera of every point in the scene. However the optical flow determined by either of the methods mentioned above will be noisy. As a result the depth information obtained from optical flow can not be successfully used in practical applications such as image segmentation 3-D reconsiruction path planning etc. By using temporal integration we can increase the accuracy of both the optical flow and the depth determined from optical flow. In this work we describe an incremental integration scheme called the running average method to temporally integrate the image flow. We integrate the depth from camera obtained using optical flow determined from gradient based methods and show that the results of temporal integration are much more useful in practical applications than the results from local edge operators. Finally we consider an image segmentation example and show the advantages of temporal integration. 1.

Paper Details

Date Published: 1 February 1991
PDF: 10 pages
Proc. SPIE 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods, (1 February 1991); doi: 10.1117/12.25226
Show Author Affiliations
Ramesh M. Rangachar, National Institute of Standards and Technology and Univ. of (United States)
Tsai Hong Hong, National Institute of Standards and Technology and American (United States)
Martin Herman, National Institute of Standards and Technology (United States)
Randall L. Luck, Aspex, Inc. (United States)


Published in SPIE Proceedings Vol. 1382:
Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods
David P. Casasent, Editor(s)

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