Page 66 - 2017 Brochure
P. 66
究員
呂俊賢 Chun-Shien Lu
Research Fellow
Ph.D., Electrical Engineering, National Cheng Kung University
Tel: +886-2-2788-3799 ext. 1513 Fax: +886-2-2782-4814
Email: lcs@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/lcs
• Deputy Director, Research Center for Information Technology Innovation, Academia Sinica (2015-present)
• Research Fellow, Institute of Information Science, Academia Sinica (2013-present)
• Associate Research Fellow, Institute of Information Science, Academia Sinica (2006-2013)
• Associate Professor (Adjunct), Computer Science and Information Engineering, National Taipei University of Technology (2006-2008)
• Assistant Research Fellow, Institute of Information Science, Academia Sinica (2002-2006)
• Ph.D., Electrical Engineering, National Cheng Kung University (1998)
• Associate Editor, IEEE Transactions on Image Processing (2010-2016)
• Ta-You Wu Memorial Award, National Science Council (2007)
Research Description Representative work on CS is summarized as follows:
(1) We have proposed a sparse Fast Fourier Transform (sFFT)
My recent research interests mainly focus on Compressive Sensing
(CS), which is a revolutionary methodology of simultaneously method that is faster than state-of-the-art methods developed at
sensing and compressing signals. We are currently interested MIT, and produces better reconstructed results. The advantages
in the issues of distributed compressive sensing (DCS), mutual of our method also include the easy selection of parameters
incoherence property, compressive sensing over graph models, and and easy implementation of sFFT without needing to know
theoretical but more practical bounds for sparse signal recovery. sparsity of a signal.
DCS is a framework that jointly considers sparsity within signal (2) Cost-efficient compressive sensing of large-scale images with
ensembles and multiple measurement vectors (MMVs). quick reconstruction of high-quality results is very challenging.
Unfortunately, the current theoretical bound of performance for We solved convex optimization via the proposed tree structure
MMVs is derived to be the same as that for single MV (SMV). sparsity pattern.
We showed that, by taking the size of signal ensembles into (3) In sparse signal recovery of compressive sensing, the phase
consideration, MMVs indeed exhibit better performance than SMV. transition determines the edge, which separates successful
We are currently deriving more complete and tight theoretical results. recovery and failed recovery. Earlier work on phase transition
For sparse signal recovery algorithms, it is very often the case that analysis in either SMV or MMVs has been too strict or ideal to
theoretical bounds are too strict to fit practical situations. Our goal be satisfied in the real world. We determined necessary and
is to close the gap between them. sufficient conditions of successful recovery from MMVs.
Publications 7. Chun-Shien Lu and Chao-Yung Hsu, “Constraint-Optimized Keypoint
Removal/Insertion Attack: Security Threat to Scale-Space Image
1. Wei-Jie, Liang, Gang-Xuan Lin, and Chun-Shien Lu, “Tree Structure Feature Extraction,” ACM Multimedia Conference (ACM MM), Oct.
Sparsity Pattern Guided Convex Optimization for Compressive 30-Nov. 02, Nara, Japan, pp. 629-638, 2012. (full paper, acceptance
Sensing of Large-Scale Images,” IEEE Trans. on Image Processing, rate 20.2%)
Vol. 26, No. 2, pp. 847-859, 2017.
8. Li-Wei Kang, Chao-Yung Hsu, Hung-Wei Chen, Chun-Shien
2. Chia-Mu Yu, Chun-Shien Lu, and Sy-Yen Kuo, “Compressed Lu, Chih-Yang Lin, and Soo-Chang Pei, “Feature-based Sparse
Sensing-Based Clone Identification in Sensor Network,” IEEE Trans. Representation for Image Similarity Assessment,” IEEE Trans. on
on Wireless Communications, Vol. 15, No. 4, pp. 3071-3084, 2016. Multimedia, Vol. 13, No. 5, pp. 1019-1030, 2011.
3. Chun-Yen Kuo, Gang-Xuan Lin, and Chun-Shien Lu, “A Necessary 9. Chao-Yung Hsu, Chun-Shien Lu, and Soo-Chang Pei, “Temporal
and Sufficient Condition for Generalized Demixing,” IEEE Signal Frequency of Flickering-Distortion Optimized Video Halftoning for
Processing Letters, vol. 22, no. 11, pp. 2049-2053, 2015. Electronic Paper,” IEEE Trans. on Image Processing, Vol. 20, No. 9,
pp. 2502-2514, 2011.
4. Yao-Tung Tsou, Chun-Shien Lu, and Sy-Yen Kuo, “MoteSec-Aware:
A Practical Secure Mechanism for Wireless Sensor Networks,” IEEE 10. Chia-Mu Yu, Yao-Tung Tsou, Chun-Shien Lu, and Sy-Yen Kuo,
Trans. on Wireless Communications, vol. 12, no. 6, pp. 2817-2829, “Constrained Function based Message Authentication for Sensor
2013. Networks,” IEEE Trans. on Information Forensics and Security, vol. 6,
no. 2, pp. 407-425, 2011.
5. Chia-Mu Yu, Yao-Tung Tsou, Chun-Shien Lu, and Sy-Yen Kuo,
“Localized Algorithms for Detection of Node Replication Attacks in
Mobile Sensor Networks,” IEEE Trans. on Information Forensics, and
Security, Vol. 8, No. 5, pp. 754-768, 2013.
6. Chao-Yung Hsu, Chun-Shien Lu, and Soo-Chang Pei, “Image Feature
Extraction in Encrypted Domain with Privacy-Preserving SIFT,” IEEE
Trans. on Image Processing, Vol. 21, No. 11, pp. 4593-4607, 2012.
64 研究人員 Research Faculty
呂俊賢 Chun-Shien Lu
Research Fellow
Ph.D., Electrical Engineering, National Cheng Kung University
Tel: +886-2-2788-3799 ext. 1513 Fax: +886-2-2782-4814
Email: lcs@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/lcs
• Deputy Director, Research Center for Information Technology Innovation, Academia Sinica (2015-present)
• Research Fellow, Institute of Information Science, Academia Sinica (2013-present)
• Associate Research Fellow, Institute of Information Science, Academia Sinica (2006-2013)
• Associate Professor (Adjunct), Computer Science and Information Engineering, National Taipei University of Technology (2006-2008)
• Assistant Research Fellow, Institute of Information Science, Academia Sinica (2002-2006)
• Ph.D., Electrical Engineering, National Cheng Kung University (1998)
• Associate Editor, IEEE Transactions on Image Processing (2010-2016)
• Ta-You Wu Memorial Award, National Science Council (2007)
Research Description Representative work on CS is summarized as follows:
(1) We have proposed a sparse Fast Fourier Transform (sFFT)
My recent research interests mainly focus on Compressive Sensing
(CS), which is a revolutionary methodology of simultaneously method that is faster than state-of-the-art methods developed at
sensing and compressing signals. We are currently interested MIT, and produces better reconstructed results. The advantages
in the issues of distributed compressive sensing (DCS), mutual of our method also include the easy selection of parameters
incoherence property, compressive sensing over graph models, and and easy implementation of sFFT without needing to know
theoretical but more practical bounds for sparse signal recovery. sparsity of a signal.
DCS is a framework that jointly considers sparsity within signal (2) Cost-efficient compressive sensing of large-scale images with
ensembles and multiple measurement vectors (MMVs). quick reconstruction of high-quality results is very challenging.
Unfortunately, the current theoretical bound of performance for We solved convex optimization via the proposed tree structure
MMVs is derived to be the same as that for single MV (SMV). sparsity pattern.
We showed that, by taking the size of signal ensembles into (3) In sparse signal recovery of compressive sensing, the phase
consideration, MMVs indeed exhibit better performance than SMV. transition determines the edge, which separates successful
We are currently deriving more complete and tight theoretical results. recovery and failed recovery. Earlier work on phase transition
For sparse signal recovery algorithms, it is very often the case that analysis in either SMV or MMVs has been too strict or ideal to
theoretical bounds are too strict to fit practical situations. Our goal be satisfied in the real world. We determined necessary and
is to close the gap between them. sufficient conditions of successful recovery from MMVs.
Publications 7. Chun-Shien Lu and Chao-Yung Hsu, “Constraint-Optimized Keypoint
Removal/Insertion Attack: Security Threat to Scale-Space Image
1. Wei-Jie, Liang, Gang-Xuan Lin, and Chun-Shien Lu, “Tree Structure Feature Extraction,” ACM Multimedia Conference (ACM MM), Oct.
Sparsity Pattern Guided Convex Optimization for Compressive 30-Nov. 02, Nara, Japan, pp. 629-638, 2012. (full paper, acceptance
Sensing of Large-Scale Images,” IEEE Trans. on Image Processing, rate 20.2%)
Vol. 26, No. 2, pp. 847-859, 2017.
8. Li-Wei Kang, Chao-Yung Hsu, Hung-Wei Chen, Chun-Shien
2. Chia-Mu Yu, Chun-Shien Lu, and Sy-Yen Kuo, “Compressed Lu, Chih-Yang Lin, and Soo-Chang Pei, “Feature-based Sparse
Sensing-Based Clone Identification in Sensor Network,” IEEE Trans. Representation for Image Similarity Assessment,” IEEE Trans. on
on Wireless Communications, Vol. 15, No. 4, pp. 3071-3084, 2016. Multimedia, Vol. 13, No. 5, pp. 1019-1030, 2011.
3. Chun-Yen Kuo, Gang-Xuan Lin, and Chun-Shien Lu, “A Necessary 9. Chao-Yung Hsu, Chun-Shien Lu, and Soo-Chang Pei, “Temporal
and Sufficient Condition for Generalized Demixing,” IEEE Signal Frequency of Flickering-Distortion Optimized Video Halftoning for
Processing Letters, vol. 22, no. 11, pp. 2049-2053, 2015. Electronic Paper,” IEEE Trans. on Image Processing, Vol. 20, No. 9,
pp. 2502-2514, 2011.
4. Yao-Tung Tsou, Chun-Shien Lu, and Sy-Yen Kuo, “MoteSec-Aware:
A Practical Secure Mechanism for Wireless Sensor Networks,” IEEE 10. Chia-Mu Yu, Yao-Tung Tsou, Chun-Shien Lu, and Sy-Yen Kuo,
Trans. on Wireless Communications, vol. 12, no. 6, pp. 2817-2829, “Constrained Function based Message Authentication for Sensor
2013. Networks,” IEEE Trans. on Information Forensics and Security, vol. 6,
no. 2, pp. 407-425, 2011.
5. Chia-Mu Yu, Yao-Tung Tsou, Chun-Shien Lu, and Sy-Yen Kuo,
“Localized Algorithms for Detection of Node Replication Attacks in
Mobile Sensor Networks,” IEEE Trans. on Information Forensics, and
Security, Vol. 8, No. 5, pp. 754-768, 2013.
6. Chao-Yung Hsu, Chun-Shien Lu, and Soo-Chang Pei, “Image Feature
Extraction in Encrypted Domain with Privacy-Preserving SIFT,” IEEE
Trans. on Image Processing, Vol. 21, No. 11, pp. 4593-4607, 2012.
64 研究人員 Research Faculty
