計算機科學技術名家講座
（2013-24-29）
講座題目：《Sparse Coding and Dictionary Learning for Signal and Image Processing》
專題講座
主 講 人：段晔博士
美國密蘇裡大學工程學院都市安全研究中心主任， 計算機系計算機圖形與圖像理解實驗室主任， 副教授、伟德国际BETVlCTOR唐敖慶講座教授。
講座時間：2013年8月14日上午9:00
1: Introduction of Regularization, Convexity, L1-minimization, L0-Norm.
2: Motivation and the Sparse Coding Algorithm.
講座時間：2013年8月15日下午14:00
3: Greedy Pursuit Algorithms: Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP), Least Squares OMP (LS-OMP), Weak Matching Pursuit (WMP), and Thresholding algorithm.
4: Dictionary Learning: the K-SVD Algorithm.
講座時間：2013年8月16日上午9:00
5: Image Denoising and Image Inpainting with a learnt Dictionary.
6: Image Compression and Compressive Sensing.
講座地點：前衛南校區計算機大樓A521報告廳
主辦單位：伟德国际BETVlCTOR
伟德国际BETVlCTOR計算機科學技術研究所
伟德国际BETVlCTOR軟件學院
符号計算與知識工程教育部重點實驗室
歡迎廣大師生踴躍參加！
Course Description:
In the past few years significant progress has been made in Sparse Representation: an efficient representations of data as a (often linear) combination of a few typical patterns (atoms) learned from the data itself. Sparse representation has led to state-of-the-art results in many signal and image processing and analysis tasks. This course will describe effective algorithms for learning such collections of atoms (usually called dictionaries or codebooks), and how to compute such sparse representations with high accuracy. We will discuss both the theoretical foundations of Sparse Representation as well as the state-of-the-art in applying these techniques in areas such as medical imaging, computer animation, computer graphics, computer vision and virtual reality, etc. We will explore current research issues and will cover in depth the associated computational and numerical techniques. This course should be appropriate for graduate students in all areas as well as advanced undergraduate students.
