| The three-dimensional human body model has being widely used in game animation, clothing CAD/CAM, medical and other fields, and the model data’s acquisition and representation is the key technology in this field. In recent years, the three-dimensional human body surface modeling technique has become the research hotspots in computer graphics, computer simulation and clothing CAD fields. For example, the Electronic Made To Measure (eMTM) have became the industry trends with the development of modern clothing industry and the digital clothing technology. The eMTM’s development objectives were to improve the accuracy of anthropometric data, accomplish the recognition and analyze body types, then finally matching human body to the garment. However, the artificial method was too time wasting and error-prone to eMTM. So non-contact 3D body measurement becomes the main way, and it provides the possibility for three-dimensional modeling, data mining and the virtual dressing. Since the traditional laser non-contact anthropometric devices are harm to the eyes and high prices, in this thesis I use a new points-cloud scan sensor which is produced by Microsoft Kinect to acquire the human body data and reconstruct the surface. Then the characterized sizes of human body model are calculated. After that,3D human body data are classified in terms of somatotypes. According to points matching method, we implement the virtual dry-on. The main research contents are as follows:1. A multi-cameras measuring system is proposed.4 Kinect devices are applied in our research. Inner and external reference parameters are confirmed by the parallel binocular vision theory, which includes the vertical axis arguments, the distance between camera’s optical center to its reference surface, and the distance between optical centers and so on.2. A novel three-dimensional surface fitting method is proposed. Firstly, in order to solve the problem of noise interference in the Kinect depth images, we utilize smooth fairing method to eliminate noise. And then variable types of holes are filled by optimal algorithm. Finally an improved Iterative Closed Points (TCP) algorithm is used to match the whole images obtained from multi-cameras. We utilize greedy projection triangulation algorithm to implement three-dimensional human body surface reconstruction.3. Human body characterized sizes measured method is proposed. A modified parabolic function with construct the membership function is proposed to confirm the positions of mark points. After that, we use pre-matching method to scan hierarchical point-layers. As well according to changing the searching steps of algorithm, we complete the extraction of characterized points, lines and faces. And the characterized sizes of model are also acquired.4. A classifier method is proposed to recognize the somatotypes. Here we composite the method of principal component analysis and kernel clustering analysis to divide the human somatotypes into six types. And then we use the GA-SVM to establish the human body identifying model. The genetic algorithm is introduced to amend the SVM parameters to improve the recognition accuracy.5. A fast matching method of virtual closes and body model is proposed. We assume the closes models and human body models are rigid models. We acquire matching vertex according to the curvature and triangle constraint method. Then we fit the surfaces using minimize target distances method. A dry-on effect is demonstrated finally. In addition, to reduce the matching errors we utilize LM algorithm to optimize the three-dimensional transforms as well as use adjusted local rigid triangle to avoid the garment being penetrated by body model.
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