Description: Sparse Representation or Collaborative Representation: Which Helps Face Recognition? This code devotes to analyze the working mechanism of SRC, and indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification. Platform: |
Size: 3300612 |
Author:674946694@qq.com |
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Description: 稀疏图像表示的课件，西安电子科大焦李成做-Sparse image representation of the courseware, Xi' an Electronic Science and Technology Li-Cheng Jiao do Platform: |
Size: 462848 |
Author:lijianhong |
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Description: 稀疏矩阵的三元组表示与转置，实现转置前后矩阵的三元组表示方法-Sparse matrix representation and triple transpose, transposed before and after implementation of the triple matrix representation Platform: |
Size: 1024 |
Author:陈阳 |
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Description: 该文对压缩感知理论进行了综述，对压缩感知的稀疏表示、观测矩阵、编码、解码和有待研究的关键问题进行了综述-This paper summarizes the theory of compressed sensing, sparse representation of compressed sensing, observation matrix, encoding, decoding and the key issues to be examined were reviewed Platform: |
Size: 97280 |
Author:成贵均 |
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Description: 图像超分辨率重构
Image Super-Resolution as Sparse Representation of Raw Image Patches-Image Super-Resolution as Sparse Representation of Raw Image Patches Platform: |
Size: 12196864 |
Author: |
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Description: Elad写的关于稀疏理论的书，内容丰富，适合初学稀疏理论的同学，不容错过额-a book about sparse representation of signal and its practice Platform: |
Size: 20974592 |
Author:autumn |
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Description: 用matlab实现的基于稀疏表示的人脸识别方法。其中解稀疏表示时，包含了各种方法。-Using matlab face recognition method based on sparse representation. Solution of the sparse representation, contains a variety of methods. Platform: |
Size: 47104 |
Author:wangbinbin |
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Description: Sparse and Redundant Representations,讲解稀疏表示的系统书籍，非常值得看-Sparse and Redundant Representations, explain the sparse representation system book, well worth watching Platform: |
Size: 14416896 |
Author:溜溜 |
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Description: In this paper, we propose a two-phase test sample
representation method for face recognition. The first phase of
the proposed method seeks to represent the test sample as
a linear combination of all the training samples and exploits
the representation ability of each training sample to determine
M “nearest neighbors” for the test sample. The second phase
represents the test sample as a linear combination of the
determined M nearest neighbors and uses the representation
result to perform classification. We propose this method with the
following assumption: the test sample and its some neighbors
are probably from the same class. Thus, we use the first phase
to detect the training samples that are far from the test sample
and assume that these samples have no effects on the ultimate
classification decision. This is helpful to accurately classify the
test sample. We will also show the probability explanation of
the proposed method. A number of face recognition experiments
show that our method performs very well. Platform: |
Size: 460458 |
Author:may@uestc.edu.cn |
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Description: 杨建超的将稀疏表达用于图像超分辨率重建的文章赋代码(Example matlab code for the algorithm proposed in "Image super-resolution via sparse representation" TIP 2010.) Platform: |
Size: 27652096 |
Author:那方贤 |
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Description: 组稀疏表达Matlab代码，同时里面包含了压缩感知信号稀疏表征的几种典型算法。(G r o u p S p a r s e B o x, the matlab codes for group sparse representation, some classical algorithms such as BMP, BOMP, StGOMP etc. also included.) Platform: |
Size: 30720 |
Author:FandyAIR |
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