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29 January 1999 Simultaneous spatiotemporal target segmentation and motion estimation in a variational formulation
Alan Q. Li, Vikram Chalana, Hongkai Zhao
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Proceedings Volume 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition; (1999) https://doi.org/10.1117/12.339834
Event: The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, 1998, Washington, DC, United States
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.
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Alan Q. Li, Vikram Chalana, and Hongkai Zhao "Simultaneous spatiotemporal target segmentation and motion estimation in a variational formulation", Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); https://doi.org/10.1117/12.339834
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KEYWORDS
Image segmentation
Optical flow
Motion estimation
Image fusion
Image processing algorithms and systems
Automatic target recognition
Expectation maximization algorithms
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