Tsung-Han Chan's Publications (Google )

• Submitted Journal/Conference Papers: (* corresponding author)

1. Y. Zhang*, K. Jia, Y. Wang, G. Pan, T.-H. Chan and Y. Ma, "Robust face recognition by constrained part-based alignment," submitted to International Journal of Computer Vision (under review). [ArXiv]

• Journal Papers: (* corresponding author)

1. X. Fu, W.-K. Ma, J. M. Bioucas-Dias, and T.-H. Chan, "Semiblind hyperspectral unmixing in the presence of spectral library mismatches," to appear in IEEE Trans. Geoscience and Remote Sensing, 2016 . [ArXiv]
2. S. Gao*, Z. Zeng, K. Jia, T.-H. Chan, and Y. Ma, "Patch set based representation for alignment-free image set classification," to appear in IEEE Trans. Circuits and Systems for Video Technology, 2016.
3. C.-H. Lin, C.-Y. Chi, Y.-H. Wang and T.-H. Chan, "A fast hyperplane-based minimum-volume enclosing simplex algorithm for blind hyperspectral unmixing," IEEE Trans. Signal Processing, vol. 64, no. 8, pp. 1946-1961, April 2016.
4. K. Jia*, T.-H. Chan, Z. Zeng, S. Gao, G. Wang, T. Zhang, and Y. Ma, "ROML: A robust feature correspondence approach for matching objects in a set of images," International Journal of Computer Vision, Sept. 2015. [ArXiv]
5. A. Ambikapathi*, T.-H. Chan, C.-H. Lin, F.-S. Yang, C.-Y. Chi, and Y. Wang, "Convex optimization-based compartmental pharmacokinetic analysis for prostate tumor characterization using DCE-MRI," IEEE Trans. Biomedical Engineering, vol. 63, no. 4, pp. 707-720, April 2016.
6. T.-H. Chan*, K. Jia, S. Gao, J. Lu, Z. Zeng, and Y. Ma, "PCANet: A simple deep learning baseline for image classification?" IEEE Trans. Image Processing, vol. 24, no. 12, pp. 5017-5032, Dec. 2015. [ArXiv] (Source Code).
7. X. Fu, W.-K. Ma*, T.-H. Chan, and J. M. Bioucas-Dias, "Self-dictionary sparse regression for hyperspectral unmixing: Greedy pursuit and pure pixel search are related," IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 6, pp. 1128-1141, Sept. 2015. [ArXiv] (Source Code)
8. L. Zhuang, T.-H. Chan, Allen Y. Yang*, S. S. Sastry, and Y. Ma, "Sparse illumination learning and transfer for single-sample face recognition with image corruption and misalignment," International Journal of Computer Vision, vol. 114, no. 2, pp. 272-287, Sept. 2015. [ArXiv] (Source Code)
9. T.-H. Chan*, K. Jia, E. Wycoff, and Y. Ma, "MMSE design of time and color multiplexing codes," IEEE Trans. Signal Processing, vol. 62, no. 5, pp. 1157-1170, Mar. 2014.
10. W.-K. Ma*, J. M. Bioucas-Dias, T.-H. Chan, N. Gillis, P. Gader, A. Plaza, A. Ambikapathi, and C.-Y. Chi, "An signal processing perspective on hyperspectral unmixing," IEEE Signal Processing Magazine, vol. 31, no. 1, pp. 67-81, Jan. 2014.
11. K.-K. Lee, W.-K Ma*, X. Fu, T.-H. Chan, and C.-Y. Chi, "A Khatri-Rao subspace approach to blind identification of mixtures of quasi-stationary sources," Signal Processing - Special Issue on Advances in Sensor Array Processing, vol. 93, no. 12, pp. 3515-3527, Dec. 2013.
12. T.-H. Chan*, A. Ambikapathi, W.-K. Ma, and  C.-Y. Chi, “Robust affine set fitting and fast simplex volume max-min for hyperspectral endmember extraction,” IEEE Trans. Geoscience and Remote Sensing, vol. 51, no. 7, pp. 3982 - 3997, July, 2013. (Source Code)
13. A. Ambikapathi, T.-H. Chan*, C.-Y. Chi, and  K. Keizer, “Hyperspectral data geometry based estimation of number of endmembers using p−norm based pure pixel identification,” IEEE Trans. Geoscience and Remote Sensing, vol. 51, no. 5, pp. 2753-2769, May 2013. (Source Code)
14. Y.-C. Lin, T.-H. Chan, C.-Y. Chi, S.-H. Ng, H.-L. Liu, K.-C. Wei, Y.-Y. Wai, C.-C. Wang and J.-J. Wang*, "Blind estimation of arterial input function in dynamic contrast-enhanced MRI using purity maximization," Magnetic Resonance in Medicine, vol. 68, no. 5, pp. 1439-1449, Nov. 2012. (supporting materials)
15. L. Chen**, T.-H. Chan**, P. L. Choyke, E. M. C. Hillman, Z. M. Bhujwalla, C.-Y. Chi, G. Wang, Z. Szabo, Y. Wang*, " CAM-CM: A signal deconvolution tool for In vivo dynamic contrast-enhanced imaging of complex tissues," Bioinformatics, vol. 27, no. 18, pp. 2607-2609, 2011 (** contributed equally). (supplementary inf.)
16. L. Chen, P. L. Choyke, T.-H. Chan, C.-Y. Chi, G. Wang, and Y. Wang*, "Tissue-specific compartmental analysis for dynamic contrast-enhanced MR imaging of complex tumors," IEEE Trans. Medical Imaging, vol. 30, no. 12, pp. 2044-2058, Dec. 2011.
17. A. Ambikapathi, T.-H. Chan*, W.-K. Ma, and C.-Y. Chi, “Chance constrained robust minimum volume enclosing simplex algorithm for hyperspectral unmixing,” IEEE Trans. Geoscience and Remote Sensing – Special Issue on Spectral Unmixing of Remotely Sensed Data, vol. 49, no. 11, pp. 4194-4209, Nov. 2011. (Source Code)
18. T.-H. Chan, W.-K. Ma*, A. Ambikapathi, and C.-Y. Chi, “A simplex volume maximization framework for hyperspectral endmember extraction,” IEEE Trans. Geoscience and Remote Sensing – Special Issue on Spectral Unmixing of Remotely Sensed Data, vol. 49, no. 11, pp. 4177-4193, Nov. 2011. (Source Code)
19. F.-Y. Wang*, C.-Y. Chi, T.-H. Chan, and Y. Wang, “Nonnegative least correlated component analysis for separation of dependent sources by volume maximization,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 875-888, May 2010. (Source Code)
20. T.-H. Chan, C.-Y. Chi*, Y.-M. Huang, and W.-K. Ma, “A convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing,” IEEE Trans. Signal Processing, vol. 57, no. 11, pp. 4418-4432, Nov. 2009. (Source Code) (Highly Cited Paper by ISI Essential Science Indicator)
21. T.-H. Chan, W.-K. Ma*, C.-Y. Chi and Y. Wang, “A convex analysis framework for blind separation of non-negative sources,” IEEE Trans. Signal Processing, vol. 56, no. 10, pp. 5120–5134, Oct. 2008. (Source Code).

• Book Chapters:

1. W.-K. Ma, T.-H. Chan, C.-Y. Chi, and Y. Wang, Convex analysis for non-negative blind source separation with application in imaging, in Chapter 7, Convex Optimization in Signal Processing and Communications, Editors: D. P. Palomar and Y. C. Eldar, UK: Cambridge University Press, 2010. (Source Code)

• Conference Papers:

1. C.-H. Lin, C.-Y. Chi, Y.-H. Wang and T.-H. Chan, "A fast hyperplane-based MVES algorithm for hyperspectral unmixing," in IEEE ICASSP, 2015.
2. F. Xiao, W.-K. Ma, J. M. Biocucas-Dias, and T.-H. Chan, "A robust subspace method for semiblind dictionary-aided hyperspectral unmixing," in IEEE WHISPERS, 2014.
3. L. Sun, K. Jia, T.-H. Chan, Y. Fang, and S. Yan, "Deeply-learned slow feature analysis for action recognition," in IEEE CVPR, 2014.
4. Y. Ai, W. Li, T.-H. Chan, and Q. Liao, "Removal of bleed-through effect via nonnegative least-correlation," in IEEE ICASSP, 2014.
5. F. Xiao, W.-K. Ma, T.-H. Chan, J. M. Bioucas-Dias, and M.-D. Iordache, "Greedy algorithms for pure pixels identification in hyperspectral unmixing: A multiple-measuerment vector viewpoint," in EUSIPCO, 2013.
6. Y. Chuo, T.-H. Chan, and M.-L. Li, "Compressed sensing study: reconstruction on different ultrasound imaging stages," in IEEE International Ultrasonics Symposium (IUS), 2013.
7. (Invited paper) A. Ambikapathi, T.-H. Chan, C.-H. Lin and C.-Y. Chi, "Convex geometry based outlier-insensitive estimation of number of endmembers in hyperspectral images," in IEEE WHISPERS, 2013.
8. Eliot Wycoff, T.-H. Chan, K. Jia, W.-K. Ma and Y. Ma, "A non-negative sparse promoting algorithm for high resolution hyperspectral imaging," in IEEE ICASSP, 2013.
9. Z. Zeng, X. Jie, K. Jia, T.-H. Chan, S. Guo, X. Dong, and Y. Ma, “Learning by associating ambiguously labeled images,” in IEEE CVPR, 2013.
10. Kui Jia, T.-H. Chan, and Yi Ma, "Robust and practical face recognition via structured sparsity," The European Conference on Computer Vision (ECCV), Firenze, Italy, Oct. 7-13, 2012.
11. T.-H. Chan, Kui Jia, Eliot Wycoff, Chong-Yung Chi and Yi Ma, "Towards optimal design of time and color multiplexing codes," The European Conference on Computer Vision (ECCV), Firenze, Italy, Oct. 7-13, 2012.
12. Zinan Zeng, T.-H. Chan, Kui Jia, and Dong Xu, "Finding correspondence from multiple images via sparse and low-rank decomposition," The European Conference on Computer Vision (ECCV), Firenze, Italy, Oct. 7-13, 2012.
13. (Invited paper) H.-E. Huang, T.-H. Chan, A. Ambikapathi, W.-K. Ma, and  C.-Y. Chi, "Outlier-robust dimension reduction and its impact on hyperspectral endmember extraction," in IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Shanghai, China, June 5-7, 2012.(Source Code)
14. Y.-S. Shen, T.-H. Chan, SeBastien Bourguignon and  C.-Y. Chi, "Spatial-spectral unmixing of hyperspectral data for detection and analysis of astrophysical sources with the MUSE instrument," in IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Shanghai, China, June 5-7, 2012.
15. A. Ambikapathi, T.-H. Chan, K. Keizer, F.-S. Yang, and C.-Y. Chi, "An nBSS algorithm for pharmacokinetic analysis of prostate cancer using DCE-MR images," in IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Barcelona, Spain, May 2-5, 2012.
16. Ambikapathi, T.-H. Chan, and C.-Y. Chi, “Convex geometry based estimation of number of endmembers in hyperspectral images,” in IEEE ICASSP, Kyoto, Japan, Mar. 25-30, 2012.
17. T.-H. Chan, J.-Y. Liou, A. Ambikapathi, W.-K. Ma and C.-Y. Chi, “Fast algorithms for robust hyperspectral endmember extraction based on worst-case simplex volume maximization,” in IEEE ICASSP, Kyoto, Japan, Mar. 25-30, 2012. (Source Code)
18. T.-H. Chan, C.-J. Song, A. Ambikapathi, C.-Y. Chi, and W.-K. Ma, "Fast alternating volume maximization algorithm for blind separation of non-negative sources," in Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Beijing, China, Sept. 18-21, 2011.
19. K.-K. Lee, W.-K. Ma, Y.-L. Chiou, T.-H. Chan, and C.-Y. Chi, "Blind identification of mixtures of quasi-stationary sources using a Khatri-Rao subspace approach," in Asilomar Conference on Signals, Systems, and Computers, Pacific Glove, CA, USA, Nov. 6-9, 2011.
20. (Best paper award) T.-H. Chan, W.-K. Ma, A. Ambikapathi, and C.-Y. Chi, "Robust endmember extraction using worst-case simplex volume maximization," in Proc. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal, June 6-9, 2011. (Source Code)
21. (Invited paper) T.-H. Chan, W.-K. Ma, A. Ambikapathi, and C.-Y. Chi, “An optimization perspective on Winter's endmember extraction belief,” in Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS'11), Sendai, Japan, Aug. 1-5, 2011.
22. A. Ambikapathi, T.-H. Chan, C.-Y. Chi, and K. Keizer, "Two effective and computationally efficient pure-pixel based algorithms for hyperspectral endmember extraction," in IEEE ICASSP, Prague, Czech Republic, May 22-27, 2011.
23. A. Ambikapathi, T.-H. Chan, W.-K. Ma, and C.-Y. Chi, “A robust alternating volume maximization algorithm for endmember extraction in hyperspectral images,” in Proc. Second IEEE WHISPERS, Reykjavik, Iceland, June 14-16, 2010.
24. A. Ambikapathi, T.-H. Chan, W.-K. Ma, and C.-Y. Chi, “A robust minimum-volume enclosing simplex algorithm for hyperspectral unmixing,” in Proc. IEEE ICASSP, Dallas, Texas, Mar. 14-19, 2010, pp. 1202-1205. (Source Code)
25. (Invited paper) T.-H. Chan, W.-K. Ma, C.-Y. Chi, and A. Ambikapathi, “Hyperspectral unmixing from a convex analysis and optimization perspective,” in Proc. First IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Grenoble, France, August 26-28, 2009, pp. 1-4. (slides)
26. T.-H. Chan, C.-Y. Chi, Y.-M. Huang and W.-K. Ma, “A convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 19-24, 2009, pp. 1089-1092. (Source Code)
27. Yitan Zhu, T.-H. Chan, E. P. Hoffman, and Y. Wang, “Gene expression dissection by non-negative well-grounded source separation,” in Proc. 2008 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Cancun, Mexico, Oct. 16-19, 2008, pp. 255-260.
28. L. Chen, T.-H. Chan, P. L. Choyke, C.-Y. Chi, G. Wang, and Y. Wang, “Convex analysis and separation of composite signals in DCE-MRI,” in Proc. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Paris, France, May 14-17, 2008, pp. 1557-1560.
29. T.-H. Chan, L. Chen, P. L. Choyke, C.-Y. Chi, G. Wang, and Y. Wang, “Convex analysis for separation of functional patterns in DCE-MRI: A longitudinal study to antiangiogenic therapy,” in Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Cancun, Mexico, Oct. 16-19, 2008, pp. 261-266.
30. T.-H. Chan, W.-K. Ma, C.-Y. Chi, and Y. Wang, “Blind separation of non-negative sources by convex analysis: Effective method using linear programming,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, March 30-April 4, 2008, pp. 3493-3496.
31. T.-H. Chan, W.-K. Ma, C.-Y. Chi, and Y. Wang, “A convex analysis based criterion for blind separation of non-negative sources,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 15-20, 2007, pp. 961-964.
32. F.-Y. Wang, C.-Y. Chi, T.-H. Chan, and Y. Wang, “Blind separation of positive dependent sources by non-negative least-correlated component analysis,” in Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Maynooth, Ireland, Sept. 6-8, 2006, pp. 73-78.
33. F.-Y. Wang, Y. Wang, T.-H. Chan, and C.-Y. Chi, “Blind separation of multichannel biomedical image patterns by non-negative least-correlated component analysis,” in Lecture Notes in Bioinformatics (Proc. PRIB’06), LNBI 4146, pp. 151-162, Springer-Verlag, Berlin, 2006.

• Patent:

1. J.-J. Wang, Y.-C. Lin, T.-H. Chan, C.-Y. Chi, S.-H. Ng, and Y.-Y. Wai, “Method for extracting arterial input function and application thereof to dynamic contrast enhanced magnetic resonance imaging ,” US Patent 20140241604, Aug. 28, 2014.

• Ph.D. dissertation:

1. Tsung-Han Chan, Convex Analysis Based Non-negative Blind Source Separation for Biomedical and Hyperspectral Image Analysis, Institute of Communications Engineering, NTHU, July, 2009.