An Efficiently Coupled Shape and Appearance Prior for Active Contour Segmentation

TL;DR

This paper introduces a new shape and appearance prior model for active contour segmentation that efficiently combines intensity profiles along contours with shape features, improving segmentation accuracy in images and videos.

Contribution

The paper presents a novel joint PCA training model that couples shape and appearance features via intensity integration along iso-contours, enhancing active contour segmentation.

Findings

Improved segmentation accuracy over Chan-Vese-based methods.

Effective application to synthetic and infrared images.

Enhanced shape-appearance correlation modeling.

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.