A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Local Invariants For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
A new scheme for automated 3D PDM construction using deformable models
Image and Vision Computing
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We propose a novel Statistical Piecewise Assembled Model (SPAM) to address the open problem of small sample size encountered when applying Point Distribution Models (PDM) in 3-D medical data analysis. Specifically, in our SPAM, the Statistical Frame Model (SFM) constructed from the salient landmarks characterizes the global topological variability of the structure. Then the landmarks are employed to partition a complex object surface into piecewise segments. After that, the Statistical deformable Piecewise surface segment Models (SPMs) are established to define the fine details of local surface shape variations. The hierarchical nature of SPAM enables it to generate much more variation modes than conventional statistical models given a very small sample size training set. The experimental results demonstrate that SPAM can achieve more accuracy rates for model representation compared with traditional Active Shape Model (ASM) and Multi-resolution ASM.