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41 results on '"Deng, Qingqiong"'

10. LSCM Based Non-rigid Registration for Craniofacial Surfaces

Author

Xie, Wenkui, Duan, Fuqing, Deng, Qingqiong, Zhou, Mingquan, Wu, Zhongke, Chang, Liang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Jacko, Julie A., editor

Published
2011
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11. View-Dependent Hierarchical Foliage Simplification

Author

Deng, Qingqiong, Zhang, Xiaopeng, Jaeger, Marc, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hui, Kin-chuen, editor, Pan, Zhigeng, editor, Chung, Ronald Chi-kit, editor, Wang, Charlie C. L., editor, Jin, Xiaogang, editor, Göbel, Stefan, editor, and Li, Eric C.-L., editor

Published
2007
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20. Aging Simulation of 3D Models Based on Curvature Analysis and Color Transition

Author

Ying Zhou, Wuyang Shui, Deng Qingqiong, Zhongke Wu, Wei Wang, and Mingquan Zhou

Subjects
Color transition, Mean curvature, business.industry, 3d model, Solid modeling, Curvature, Rendering (computer graphics), symbols.namesake, Gaussian curvature, symbols, Computer vision, Statistical physics, Artificial intelligence, Curvature analysis, business, ComputingMethodologies_COMPUTERGRAPHICS, Mathematics
Abstract

Aging simulation is one of the hot issues in the field of realistic rendering. This paper presents a method to simulate the aging phenomena of 3D models with the combination of curvature analysis and color transition. For a given 3D model, we analyze the curvature of each vertex, so as to determine its global geometrical features. And then on the assumption that different areas reflect diversely in the process of aging, we use curvature as a determining factor as we make color transition rules to simulate the visual effect of a certain aging phenomenon. The experimental results show that our method can achieve a satisfying sense of reality at a low time expense.

Published
2015

33. Craniofacial similarity analysis through sparse principal component analysis.

Author

Li, Jinhua, Li, Xiaona, Zhao, Junli, Duan, Fuqing, Wu, Zhongke, Deng, Qingqiong, Zhou, Mingquan, and Pan, Zhenkuan

Subjects
CRANIAL manipulation, PLASTIC surgery, PRINCIPAL components analysis, CRIMINAL investigation, FACE perception
Abstract

The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery. The evaluation of craniofacial reconstruction results is important for improving the effect of craniofacial reconstruction. Here, we used the sparse principal component analysis (SPCA) method to evaluate the similarity between two sets of craniofacial data. Compared with principal component analysis (PCA), SPCA can effectively reduce the dimensionality and simultaneously produce sparse principal components with sparse loadings, thus making it easy to explain the results. The experimental results indicated that the evaluation results of PCA and SPCA are consistent to a large extent. To compare the inconsistent results, we performed a subjective test, which indicated that the result of SPCA is superior to that of PCA. Most importantly, SPCA can not only compare the similarity of two craniofacial datasets but also locate regions of high similarity, which is important for improving the craniofacial reconstruction effect. In addition, the areas or features that are important for craniofacial similarity measurements can be determined from a large amount of data. We conclude that the craniofacial contour is the most important factor in craniofacial similarity evaluation. This conclusion is consistent with the conclusions of psychological experiments on face recognition and our subjective test. The results may provide important guidance for three- or two-dimensional face similarity evaluation, analysis and face recognition. [ABSTRACT FROM AUTHOR]

Published
2017
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