3D Shape Modeling Employing Fuzzy Clustering for Stereo Vision

In this paper, a novel fuzzy-clustering-based method for 3D shape modeling is proposed. This method is intended for scenes involving multiple objects, where each object is replaced by a primitive model. The proposed method is composed of three stages. In the first stage, 3D data is reconstructed using stereo matching technique from a stereo image taking multiple objects. Next, the 3D data is divided into a single object by employing a fuzzy c-means augmented with principal component analysis (PCA) and a criterion about the number of clusters. Finally, the shape of each object is extracted by fuzzy c-varieties with noise clustering.