Research On Human Body Segmentation In Static Images

Human body segmentation is to segment the human body from the static images which in-clude human body, and can be applied to many tasks of computer vision, such as human action understanding, content-based image coding, background removing, figure pose classification and so on. However, the body segmentation remains a challenging problem in computer vision. Difficulties arise from various body posture, different color and texture of clothes, various light-ing conditions and cluttered backgrounds. Generally speaking, the work can be divided into two categories:interaction method and automatic method. The main contribution is listed in the following:First, for interaction method, the traditional method uses the user labeled foreground and background seeds to initialize terminal links (t-links) and neighborhood links (n-links). This thesis proposes a novel strategy to consider the initialization of t-links and n-links simultane-ously with similarity graph. To achieve the goal, this thesis employs the complete graph and sparse coding to construct the graph for interaction method of human body. The experiments show that the proposed strategy is robust to complex background and parameter changes.Second, for automatic human body segmentation, this thesis proposes model based method. According to maximum a posteriori (MAP), this thesis proposes torso models and upper leg models to detect the torso and upper leg and provide the seeds of graph cut and reference of Independent component analysis with reference (ICA-R). This kind method can fully employ the top knowledge of models and the locality property of superpixel to finish human body seg-mentation efficiently.Third, this thesis proposes the method based on hierarchical searching tree for human body segmentation. This method proposes the model of adjacent parts, which corresponds to the node of tree according to the tree theory. As a result, the pose is modeled as the summary of nodes of a path from the tree foot to the leaf. This method can effectively avoid the dependency of part detector and the dependency of the hierarchy of hierarchical scheme.Finally, this thesis proposes the Expectation-Maximization (EM) method. This thesis pro-poses an EM based human body segmentation method according to the principle of EM. This method employs the iterate property of EM to refine the pose probability map obtained from pictorial structure model to finish the human body segmentation, which has no requirement of poses and can efficiently achieve the body segmentation.