Matching-constrained active contours with affine-invariant shape prior
Chan, Kap Luk
Date of Issue2014
School of Electrical and Electronic Engineering
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.
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Computer vision and image understanding
© 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].