3D Volume Data Segmentation from Superquadric Tensor Analysis

The segmentation of 3D target objects into coherent subregions is one of the most important issues in computer graphics as it is applied in many applications, such as medical model visualization and analysis, 3D model retrieval and recognition, skeleton extraction, and collision detection. The goal of 3D segmentation is to separate the volume or mesh data into several subregions which have similar characteristics. In this paper, we present an efficient and accurate 3D model segmentation methodology by merging and splitting the subregions in a 3D model. Our innovative 3D model segmentation system consists of two steps: i) the ellipsoidal decomposition of unorganized 3D object using properties of three dimensional second-order diffusion tensor fields, and ii) The iteratively merging and splitting of subregions of the 3D model by measuring the similarity between neighboring regions. Experimental results are conducted to evaluate the performance of our methodology using 3D models from well-known databases and 3D target objects that are reconstructed from image sequences