High-Dimensional and Low-Quality Visual Information Processing, Softcover reprint of the original 1st ed. 2015
From Structured Sensing and Understanding
Springer Theses Series

This thesis primarily focuses on how to carry out intelligent sensing and understand the high-dimensional and low-quality visual information. After exploring the inherent structures of the visual data, it proposes a number of computational models covering an extensive range of mathematical topics, including compressive sensing, graph theory, probabilistic learning and information theory. These computational models are also applied to address a number of real-world problems including biometric recognition, stereo signal reconstruction, natural scene parsing, and SAR image processing.

Bio Yue Deng received the B.E. degree (Hons.) in automatic control from Southeast University, Nanjing,
China, in 2008 and Ph.D. degree (Hons.) in control science from the Department of Automation, Tsinghua University, Beijing, China, in 2013. He was a Visiting Scholar with the School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, from 2010 to 2011. He is currently an Associate Professor with the School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
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Dr. Deng’s current research interests include computer vision, machine learning and computational biology. He is the first author of more than 10 papers published on the worldwide reputable journals and conferences. He was a recipient of the Microsoft global fellowship for young computer scientist in 2010, National Ph.D Scholarship in 2012, best control award of International Air Robotic competition in 2012. His dissertation was nominated as the outstanding Ph.D thesis by Tsinghua University in 2013.

Honors:
• Outstanding Ph.D thesis award of Tsinghua University, 2013
• Best Control Award of International Aerial Robotic Competition, 2012
• National Ph.D Fellowship, 2012
• First Class Scholarship of Tsinghua University, 2012
• Microsoft Global Fellowship for young Computer Scientist, 2010.
• First class award in Tsinghua-UC, Berkeley Global Technical Challenge Competition, 2010.
• Rockwell Global Scholarship, 2006.

Publications:
• Yue Deng, Q. Dai, R. Liu, Z. Zhang and S.Hu, "Low-Rank Structure Learning via Non-convex Heuristic Recovery", IEEE Transactions on Neural Network and Learning Systems, March, Pages 383-396, 2013 (Spotlight Paper)
• Yue Deng, Y. Liu, Q. Dai, Z. Zhang and Y. Wang, "Noisy Depth Maps Fusion for Multiview Stereo Via Matrix Completion", IEEE Journal of Selected Topics in Signal Processing, (IEEE J-STSP) Sep. Issue, 2012.
• Yue Deng, Q. Dai and Z. Zhang, "Graph Laplace for