Segmentation of Human Body Parts Using Deformable Triangulation

Abstract

This paper presents a new segmentation algorithm to segment a body posture into different body parts using the technique of triangulation. For well analyzing each posture, we first propose a triangulation-based method to triangulate it to different triangle meshes. Then, we use a depth-first search scheme to find a spanning tree as its skeleton feature from the set of triangulation meshes. The triangulation-based scheme to extract important skeleton features has more robustness and effectiveness than other silhouette-based approaches. Then, different body parts can be roughly extracted by removing all the branching points from the spanning tree. A model-driven technique is then proposed for more accurately segmenting a human body into semantic parts. This technique uses the concept of Gaussian mixture model (GMM) to model different visual properties of different body parts. Then, a suitable segmentation scheme can be driven by classifying these models using their skeletons. Experimental results have proved that the proposed method is robust, accurate, and powerful in body part segmentation