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sparse representation algorithm
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Matching Pursuit方法,经典的稀疏表示方法,可以用人脸识别和图像分类,图像去噪,现在非常流行。-Matching Pursuit method, sparse representation of the classic, you can use face recognition and image classification, image denoising, now very popular.
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数字图像处理,K-SVD字典学习方法,信号的稀疏与冗余表示理论,图像压缩,图像去噪-Digital image processing, K-SVD dictionary learning methods, sparse and redundant signal representation theory, image compression, image denoising
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稀疏表达及其应用的简单介绍,其中涵盖了稀疏表示、特征提取、压缩感知、图像增强、盲源分离、模式分类、目标跟踪和图像超分辨等。PPT和PDF是对应的,并添加了可视化的结果。-Sparse Representation and Its Application: Compressive Sensing, Visual Feature, Image Enhancement, Blind Source Separation, Pattern Classification, Object Tracking a
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A hybrid approach combining extreme learning machine and sparse representation for image classification
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A new fingerprint compression algorithm based on
sparse representation is introduced. Obtaining an overcomplete
dictionary from a set of fingerprint patches allows us to represent
them as a sparse linear combination of dictionary atoms.
In th
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完备图像稀疏表示是一种最新的图像表示模型,采用过完备字典中原子的线性组合形式实现图像的稀疏表示.传统
的过完备图像稀疏表示模型采用重建误差的平方和作为保真项.该保真项没有充分考虑到人眼对图像的感知特性,无法度量图
像中边缘、轮廓、纹理等局部几何结构的变化.本文基于过完备稀疏表示理论思想,建立了新的稀疏性正则化的图像稀疏表示模
型.模型中的正则项约束图像表示系数的稀疏性,保真项采用更符合视觉感知的结构相似性度量.基于正交匹配追踪算法,提出
了基于结构相似度的正交匹配追踪算法.实验结
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图像稀疏表示近来比较流行!本文是一篇基于k-svd方法变换的快速方法的经典文章!-Image sparse representation of the more popular lately! This article is based on a quick way to change the method of k-svd classic article!
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基于Gabor特征和字典学习的高斯混合稀疏表示图像识别-Image recognition based on Gabor features and dictionary learning of Gauss hybrid sparse representation
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