An Efficiently Coupled Shape and Appearance Prior for Active Contour Segmentation

• URL: http://arxiv.org/abs/2103.14887v2
• Date: Wed, 31 Mar 2021 00:45:20 GMT
• Title: An Efficiently Coupled Shape and Appearance Prior for Active Contour Segmentation
• Authors: Martin Mueller and Navdeep Dahiya and Anthony Yezzi
• Abstract summary: This paper proposes a novel training model based on shape and appearance features for object segmentation in images and videos. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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