An Efficiently Coupled Shape and Appearance Prior for Active Contour Segmentation
This work addresses segmentation accuracy for computer vision applications, but it appears incremental as it builds upon existing active contour methods with specific feature enhancements.
The paper tackled object segmentation in images and videos by proposing a novel training model that efficiently couples shape and appearance features, resulting in improved accuracy compared to Chan-Vese energy-based methods in experiments on synthetic and infrared images.
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.