Method of multi-view modeling for 3D target based on SVDD

Popular 3D target recognition approaches based on image continue to struggle with challenge-viewpoint sensitivity. Multi-view modeling technique for 3D target offers promise for this challenge. A method of modeling 3D target based on support vector data description (SVDD) that could obtain a tight description covering most of the target feature data was proposed. Target's images were captured with uniform grid on the viewing sphere and characterized by feature vectors. The support vectors representing characteristic views were obtained by applying SVDD to optimize the parameters of the minimal hyper-sphere which covers as many feature vectors as possible. The experiments were conducted by applying the proposed method to an image set (each target includes 2592 images) characterized by normalized moment invariants. The results show that the proposed method is effective and feasibility.