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An Efficiently Coupled Shape and Appearance Prior for Active Contour Segmentation

2021-03-27 12:14:04
Martin Mueller, Navdeep Dahiya, Anthony Yezzi

Abstract

This paper proposes a novel training model based on shape and appearance features for object segmentation in images and videos. Whereas most such models rely on two-dimensional appearance templates or a finite set of descriptors, our appearance-based feature is a one-dimensional function, which is efficiently coupled with the object's shape by integrating intensities along the object's iso-contours. Joint PCA training on these shape and appearance features further exploits shape-appearance correlations and the resulting training model is incorporated in an active-contour-type energy functional for recognition-segmentation tasks. Experiments on synthetic and infrared images demonstrate how this shape and appearance training model improves accuracy compared to methods based on the Chan-Vese energy.

Abstract (translated)

URL

https://arxiv.org/abs/2103.14887

PDF

https://arxiv.org/pdf/2103.14887.pdf


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