Deformable-Model Based Textured Object Segmentation

Abstract

In this paper, we present a deformable-model based solution for segmenting objects with complex texture patterns of all scales. The external image forces in traditional deformable models come primarily from edges or gradient information and it becomes problematic when the object surfaces have complex large-scale texture patterns that generate many local edges within a same region. We introduce a new textured object segmentation algorithm that has both the robustness of model-based approaches and the ability to deal with non-uniform textures of both small and large scales. The main contributions include an information-theoretical approach for computing the natural scale of a “texon” based on model-interior texture, a nonparametric texture statistics comparison technique and the determination of object belongingness through belief propagation. Another important property of the proposed algorithm is in that the texture statistics of an object of interest are learned online from evolving model interiors, requiring no other a priori information. We demonstrate the potential of this model-based framework for texture learning and segmentation using both natural and medical images with various textures of all scales and patterns.