Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107217
Title: Matching-constrained active contours with affine-invariant shape prior
Authors: Yeung, Sai-Kit
Wang, Junyan
Chan, Kap Luk
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2014
Source: Wang, J., Yeung, S.-K., & Chan, K. L. (2014). Matching-constrained active contours with affine-invariant shape prior. Computer vision and image understanding, 132, 39-55.
Series/Report no.: Computer vision and image understanding
Abstract: In the object segmentation by active contours, an initial contour provided by user is often required. This paper extends the conventional active contour model by incorporating feature matching in the formulation for automatic object segmentation, yielding a novel matching-constrained active contour. The key to our formulation is a mathematical model of the relationship between interior feature points and object shape, called the interior-points-to-shape relation. According to this interior-points-to-shape relation, we are able to achieve the automatic object segmentation in two steps. Specifically, we are able to estimate the object boundary position given the matched interior feature points. Afterwards, we are able to further optimize the boundary position in the active contour framework. To obtain a unified optimization model for this task, we additionally formulate the matching score as a constraint to active contour model, resulting in our matching-constrained active contour. We also derive the projected-gradient descent equations to solve the constrained optimization. In the experiments, we show that our method achieves automatic object segmentation, and it clearly outperforms the related methods.
URI: https://hdl.handle.net/10356/107217
http://hdl.handle.net/10220/25256
ISSN: 1077-3142
DOI: 10.1016/j.cviu.2014.11.002
Schools: School of Electrical and Electronic Engineering 
Rights: © 2014 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Computer Vision and Image Understanding, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.cviu.2014.11.002].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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