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Proceedings Paper

Simultaneous spatiotemporal target segmentation and motion estimation in a variational formulation
Author(s): Alan Q. Li; Vikram Chalana; Hongkai Zhao
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Paper Abstract

In this paper, we study the problem of estimating and segmenting the optical flow field in image sequences. A variational framework based on the Mumford-Shah functional is introduced for simultaneous edge preserved optical flow estimation and motion-based segmentation. The proposed energy functional for optical flow field and its corresponding edge set if formulated to have three additive terms. The first and second terms measure the deviation from the optical flow constraints over the whole image and its smoothness at all the non-edge locations in L2 norm, respectively, while the third term regularizes the total length of all the edges. The minimization of this functional is carried out by the vector graduated nonconvexity (VGNC) algorithm with the gradient descent iterating scheme. This framework is then extended to fuse spatio-temporal segmentation by adding two more terms for spatial segmentation in the above formulation. One term is the L2 difference between the original image and an approximation of the image, while the other is the regularization of approximate image at all non-edge locations. The same VGNC procedure is performed to minimized the functional to obtain the optical flow field, the piecewise smooth image, and the spatio-temporal edge image. We illustrate the presented method and its numerical implementation on tactical image sequences.

Paper Details

Date Published: 29 January 1999
PDF: 11 pages
Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); doi: 10.1117/12.339834
Show Author Affiliations
Alan Q. Li, MathSoft, Inc. (United States)
Vikram Chalana, MathSoft, Inc. (United States)
Hongkai Zhao, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 3584:
27th AIPR Workshop: Advances in Computer-Assisted Recognition
Robert J. Mericsko, Editor(s)

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