A new method for solving the tiling problem of surface reconstruction is proposed. The proposed method uses a snake algorithm to segment the original images, the contours are then transformed into strings by Freeman＇...A new method for solving the tiling problem of surface reconstruction is proposed. The proposed method uses a snake algorithm to segment the original images, the contours are then transformed into strings by Freeman＇ s code. Symbolic string matching technique is applied to establish a correspondence between the two consecutive contours. The surface is composed of the pieces reconstructed from the correspondence points. Experimental results show that the proposed method exhibits a good behavior for the quality of surface reconstruction and its time complexity is proportional to mn where m and n are the numbers of vertices of the two consecutive slices, respectively.展开更多
In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the ...In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree （MST） on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.展开更多
文摘A new method for solving the tiling problem of surface reconstruction is proposed. The proposed method uses a snake algorithm to segment the original images, the contours are then transformed into strings by Freeman＇ s code. Symbolic string matching technique is applied to establish a correspondence between the two consecutive contours. The surface is composed of the pieces reconstructed from the correspondence points. Experimental results show that the proposed method exhibits a good behavior for the quality of surface reconstruction and its time complexity is proportional to mn where m and n are the numbers of vertices of the two consecutive slices, respectively.
基金It was supported in part by the National Natural Science Foundation of China under Grant Nos. 61472170, 61170143, 60873110, and Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia under Grant No. ITSM201301. Acknowledgement The work presented in this paper was done during Xiao-Peng Sun＇s visit at the graphics group of Michigan State University. Thank University of North Dakota for the biometrics database, thank Dr. Yi-Ying Tong for helpful discussions and review, and thank the reviewers of CVM2015 for constructive comments.
文摘In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree （MST） on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.
