Self-assembly is the process that the component form an ordered form or structure. Because of the biochemical characteristics of DNA molecules, they become a research emphasis in the field of selfassembly. DNA-based s...
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In recent years, Text-To-Speech (TTS) technology has developed rapidly. People have also been paying more attention to how to narrow the gap between synthetic speech and real speech, hoping that synthesized speech can...
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In order to improve the accuracy of hand pose estimation from a depth image, a method based on convolutional neural network (CNN) is proposed in this paper. First of all, we modify the structure of traditional CNN to ...
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The segmentation of motion capture data is to separate the different types of human motion data contains long movement sequence into motion clips with independent semantics in order to facilitate the storage in the da...
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Extracting term translation pairs is of great help for Chinese historical classics translation since term translation is the most time-consuming and challenging part in the translation of historical classics. However,...
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In order to protect image effectively, presented an image encryption algorithm based on wavelet function and four-dimension chaotic system. The algorithm firstly uses Wavelet function chaotic maps to scramble the imag...
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As a fundamental technique in data-driven (or example-based) methods, motion blending has been employed to produce new motion clip from two or more clips or introduced as a medium to improve the final result of other ...
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The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitativ...
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This paper presents a new wavelet domain watermarking method, the carrier image using wavelet decomposition. After the Walsh transform low frequency wavelet coefficients, calculated by block adaptive embedding strengt...
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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 ...
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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.
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