'Sparse representation' presentation slideshows

Sparse Representation for Image Reconstruction, and Face Recognition?

Sparse Representation for Image Reconstruction, and Face Recognition?. Lei Zhang Dept. of Computing The Hong Kong Polytechnic University http://www.comp.polyu.edu.hk/~cslzhang. My Recent Research Focus. Sparse Representation and Low Rank Image Reconstruction/Restoration

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Distributed Multi-Scale Data Processing for Sensor Networks

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Sparse Modeling of Data and its Relation to Deep Learning

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Faster TF-IDF

Faster TF-IDF. David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/ lecture6-tfidf. ppt. Administrative. Assignment 1 Assignment 2 Look at the assignment by Wed! New turnin procedure Class participation. Stoplist and dictionary size.

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Yan Cui 2013.1.16

A novel supervised feature extraction and classification framework for land cover recognition of the off-land scenario. Yan Cui 2013.1.16. The related work 2. The integration algorithm framework 3. Experiments. The related work Locally linear embedding

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Entropy, Information and Compressive Sensing in the Quantum Domain.

Entropy, Information and Compressive Sensing in the Quantum Domain. John Howell Greg Howland James Schneeloch Daniel Lum Sam Knarr Clemente Cuevas(REU) Matt Ware (REU). Robert Boyd Cliff Chan Petros Zerom. Outline. Introduction to compressive sensing Shannon entropy Nyquist sampling

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Heping Song , Tong Liu, Xiaomu Luo and Guoli Wang

Heping Song , Tong Liu, Xiaomu Luo and Guoli Wang. IEEE Inter. Conf. on Networking, Architecture, and Storage (NAS 2011) July 28-30, 2011, Dalian, China. Feedback based Sparse Recovery for Motion Tracking in RF Sensor Networks. Outline. 3. Introduction. Linear Model. Motivation.

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Image processing based on sparse representation

Image processing based on sparse representation. http://lear.inrialpes.fr/people/mairal/resources/pdf/ERMITES10.pdf. Thanks Any Questions?.

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Sparse and Redundant Representation Modeling for Image Processing

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ROBUST VISUAL TRACKING A Brief Summary

ROBUST VISUAL TRACKING A Brief Summary. 1. Gagan Mirchandani School of Engineering, University of Vermont. 1. And Ben Schilling, Clark Vandam, Kevin Haupt. Algorithms from [1],[2]. Examples from [2]. Videos from [3]. [1] J.Wright, A.Y.Yang, A.Ganesh, S.S.Sastry and Y.Ma,

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Sparse Representation

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Structured Face Hallucination

Structured Face Hallucination. Chih-Yuan Yang Sifei Liu Ming-Hsuan Yang Electrical Engineering and Computer Science. Outline. Motivation Related work Proposed method Experimental results Conclusions. Motivation. Algorithm. Generate high-quality face images. Challenges.

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Learning an Attribute Dictionary for Human Action Classification

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Compressed sensing

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UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing

UWB Echo Signal Detection With Ultra-Low Rate Sampling Based on Compressed Sensing. Contents. 1. Introduction 2. UWB Signal processing 3. Compressed Sensing Theory 3.1 Sparse representation of signals 3.2 AIC (analog to information converter)

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Highlights Learning-based local features that replace and surpass HOG.

Histograms of Sparse Codes for Object Detection. Xiaofeng Ren (Amazon, formerly Intel Labs) and Deva Ramanan (University of California, Irvine). Highlights Learning-based local features that replace and surpass HOG.

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Randomface : test image; : sparse indicator for identity

Simultaneous Image Transformation and Sparse Representation Recovery. Junzhou Huang 1 , Xiaolei Huang 2 , Dimitris Metaxas 1. 1 Division of Computer and Information Sciences, Rutgers University, NJ, USA 2 Department of Computer Science and Engineering, Lehigh University, PA, USA.

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