陳佩

Ph. D., Monash Uni, 2004; Ph. D., SJTU, 2001

He worked as a postdoctor at Monash University for about half year, as a senior research engineer at Motorola Labs (Shanghai) for about two years, then as a research professor at Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences, for about two years. Since Sept. 2008, he has moved to Sun Yat-sen University, as a professor at School of Information Science & Technology until Dec. 2015, and afterward as a professor at School of Data & Computer Science.

● ● Z. Liang and P. Chen, Delta-Density based clustering with a Divide-and-Conquer strategy: 3DC clustering, Pattern Recognition Letters, vol. 73, pp. 52-59, 2016.

● ● J. Cao, P. Chen, B. W. Ling, Z. Yang and Q. Dai, Spectral clustering with sparse graph construction based on Markov random walk, KSII Trans. on Internet and Information Systems, vol. 9, no. 7, pp. 2568-2584, 2015.

● ● J. Cao, P. Chen, Q. Dai and W.K. Ling, Local information-based fast approximate spectral clustering, Pattern Recognition Letters, vol. 38, pp. 63-69, 2014.

● ● J. Cao, P. Chen, Y. Zheng, and Q. Dai, A max-flow-based similarity measure for spectral clustering, ETRI Journal, vol. 35, no. 2, pp. 311-320, 2013.

● ● Y. Zheng, P. Chen, Y. He, J. Sun and H. Hu,Spatially consistent exemplar-based clustering, ICME ,2013.

● ● Y. Zheng and P. Chen, Clustering based on enhanced alpha-expansion move, IEEE Trans. Knowledge and Data Engineering, vol. 25, no. 10, pp. 2206-2216, 2013.

● ● G. Lian, J-H Lai, C. Y. Suen and P. Chen, Matching of tracked pedestrians across disjoint camera views using CI-DLBP, IEEE Trans. Circuits and Systems for Video Technology, vol. 22, no. 7, pp. 1087-1099, 2012.

● ● Y. Zheng, P. Chen and J-Z. Cao, MAP inference based on extended junction tree representation, CVPR, 2012.

● ● P. Chen, Hessian matrix vs. Gauss-Newton Hessian matrix, SIAM J. Numerical Analysis, vol. 49, no. 4, pp. 1417-1435, 2011.

● ● P. Chen, Why not use the LM method for fundamental matrix estimation? IET Computer Vision, vol. 4, no. 4, pp. 286-294, 2010.

● ● P. Chen and D. Suter, Rank constraints for homographies over two views: Revisiting the rank four constraint, Int'l J. Computer Vision,

● ● vol. 81, no. 2, pp. 205-225, 2009.

● ● P. Chen and D. Suter, Simultaneously estimating the fundamental matrix and homographies, IEEE Trans. Robotics,

● ● vol. 25, no. 6, pp. 1425-1431, 2009.

● ● P. Chen and D. Suter, Error analysis in homography estimation by first order approximation tools: A general technique, J. Mathematical Imaging and Vision, vol 33, no. 3, pp. 281-295, 2009.

● ● P. Chen, Optimization algorithms on subspaces: Revisiting missing data problem in low-rank matrix, Int'l J. Computer Vision,vol. 80, no. 1, pp. 125-142, 2008.

● ● P. Chen, Heteroscedastic low-rank matrix approximation by the Wiberg algorithm, IEEE Trans. Signal Processing,vol. 56, no. 4, pp. 1429-1439, 2008.

● ● P. Chen and D. Suter, A bilinear approach to the parameter estimation of a general heteroscedastic linear system, with application to conic fitting, J. Mathematical Imaging and Vision, vol 28, no. 3, pp. 191-208, 2007.

● ● P. Chen and D. Suter, An analysis of linear subspace approaches for computer vision and pattern recognition, Int'l J. Computer Vision, vol. 68, no. 1, pp. 83-106, 2006.

● ● P. Chen and D. Suter, Subspace-based face recognition: outlier detection and a new distance criterion, Intl. J. of Pattern Recognition and Artificial Intelligence, vol 19, no. 4, pp.479-493, 2005.

● ● P. Chen and D. Suter, Recovering the missing components in a large noisy low-rank matrix: Application to SFM, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1051-1063, 2004.