The fundamental step to get a Statistical Shape Model (SSM) is to align all the training samples to the same spatial modality. In this paper, we propose a new 3D alignment method for organic training samples matching, whose modalities are orientable and surface figures could be recognized. It is a feature based alignment method which matches two models depending on the distribution of surface curvature. According to the affine transformation on 2D Gaussian map, the distances between the corresponding parts on surface could be minimized. We applied our proposed method on 5 cases left lung training samples alignment and 4 cases liver training samples alignment. The experiment results were performed on the left lung training samples and the liver training samples. The availability of proposed method was confirmed.
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机译:Closure to 'Skeletonizing Pipes in Series within Urban Water Distribution Systems Using a Transient-Based Method' by Yuan Huang, Feifei Zheng, Huan-Feng Duan, Tuqiao Zhang, Xinlei Guo, and Qingzhou Zhang
机译:Discussion of 'Skeletonizing Pipes in Series within Urban Water Distribution Systems Using a Transient-Based Method' by Yuan Huang, Feifei Zheng, Huan-Feng Duan, Tuqiao Zhang, Xinlei Guo, and Qingzhou Zhang
机译:mapping soil organic carbon using auxiliary environmental covariates in a typical watershed in the Loess plateau of China: a comparative study based on three kriging methods and a soil land inference model (soLIm)