| Bernoulli Embedding Model and Its Application in Texture Mapping |
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| 作者姓名： | Hong-Xin Zhang Ying Tang Hui Zhao and Hu-Jun Bao |
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| 作者单位： | [1]State Key Lab of CAD＆CG, Zhejiang University, Hangzhou 310027, P.R. China [2]Automation Department, Xi＇an Jiaotong University, Xi＇an 710049, P.R. China |
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| 基金项目： | A preliminary version of this paper appeared in Proc. the 1st Korea-China Joint Conference on Geometric and Visual Computing. This work is supported in part by the National Basic Research 973 Program of China （Grant No.2002CB312102） and the National Natural Science Foundation of China （Grant Nos. 60021201, 60505001 and 60133020）. |
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| 摘 要： | A novel texture mapping technique is proposed based on nonlinear dimension reduction, called Bernoulli logistic embedding （BLE）. Our probabilistic embedding model builds texture mapping with minimal shearing effects. A log-likelihood function, related to the Bregman distance, is used to measure the similarity between two related matrices defined over the spaces before and after embedding. Low-dimensional embeddings can then be obtained through minimizing this function by a fast block relaxation algorithm. To achieve better quality of texture mapping, the embedded results are adopted as initial values for mapping enhancement by stretch-minimizing. Our method can be applied to both complex mesh surfaces and dense point clouds.
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| 关 键 词： | 纹理映射 Bernoulli嵌入式模型 逻辑似然函数 BLE |
| 收稿时间： | 2006-01-18 |
| 修稿时间： | 2006-01-18 |
Bernoulli Embedding Model and Its Application in Texture Mapping |
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| Hong-Xin Zhang,Ying Tang,Hui Zhao,and Hu-Jun Bao.Bernoulli Embedding Model and Its Application in Texture Mapping[J].Journal of Computer Science and Technology,2006,21(2):199-203. |
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| Authors: | Hong-Xin Zhang Ying Tang Hui Zhao Hu-Jun Bao |
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| Affiliation: | (1) State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310027, P.R. China;(2) Automation Department, Xi'an Jiaotong University, Xi'an, 710049, P.R. China |
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| Abstract: | A novel texture mapping technique is proposed based on nonlinear dimension reduction, called Bernoulli logistic embedding (BLE).Our probabilistic embedding model builds texture mapping with minimal shearing effects. A log-likelihood function, related to the Bregman distance, is used to measure the similarity between two related matrices defined over the spaces before and after embedding. Low-dimensional embeddings can then be obtained through minimizing this function by a fast block relaxation algorithm. To achieve better quality of texture mapping, the embedded results are adopted as initial values for mapping enhancement by stretch-minimizing. Our method can be applied to both complex mesh surfaces and dense point clouds. |
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| Keywords: | dimension reduction Bernoulli logistic embedding texture mapping parameterization |
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