Abstract: A method for analyzing a shape of a 3D model based on perceptive information comprises: decomposing the shape of the 3D model to generate decomposition results; and extracting a skeleton from the decomposition results. This invention can be applied to shape decomposition of objects having different shapes. The 3D models can be regular or with noise, containing either multiple annular structures or no annular structure. The decomposition method of this invention is not sensitive to noise, and the segmentation speed is high and accurate. The segmentation result of the invention can be widely applied to different branches of computer graphics and computer vision, such as computer animation, modeling, shape analysis, shape classification, object identification, etc. The skeleton extracted from the decomposition result and the following shape semantic description diagram can he applied to 3D model retrieval, model semantic analysis and so on.

Abstract: A multi-granularity parallel FFT computation device including three memories, a butterfly computation device, a state control unit, a data reversing network and a first selector. The three memories are each a multi-granularity parallel memory, and store butterfly group data and twiddle factors corresponding to the butterfly group data. The butterfly computation device perform computations of a butterfly group based on the butterfly group data outputted from the first selector and the corresponding twiddle factors outputted from one of the memories, and write a computation result back to the other two memories. The device can read butterfly group data and corresponding twiddle factors in parallel from the multi-granularity parallel memories with a specific R/W granularity. No memory conflict will occur in the read operation, and no additional process is required for sorting the read/written data.

Abstract: A method for brain tumor segmentation in multi-parametric 3D magnetic resonance (MR) images, comprising: determining, for each voxel in the multi-parametric 3D MR image sequence, a probability that the voxel is part of brain tumor; extracting multi-scale structure information of the image; generating multi-scale tumor probability map based on initial tumor probability at voxel level and multi-scale structure information; determining salient tumor region based on multi-scale tumor probability map; obtaining robust initial tumor and non-tumor label based on tumor probability map at voxel level and salient tumor region; and generating a segmented brain tumor image using graph based label information propagation. The present invention is capable of achieving statistical reliable, spatially compact, and robust tumor label initialization, which is helpful to the accurate and reliable tumor segmentation.