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数据结构的C++描述
- 目 录 译者序 前言 第一部分 预备知识 第1章 C++程序设计 1 1.1 引言 1 1.2 函数与参数 2 1.2.1 传值参数 2 1.2.2 模板函数 3 1.2.3 引用参数 3 1.2.4 常量引用参数 4 1.2.5 返回值 4 1.2.6 递归函数 5 1.3 动态存储分配
基于OMP算法的信号稀疏分解（Gabor字典）
- 基于OMP算法的信号稀疏分解程序
OMP算法：匹配追踪算法
- OMP算法：匹配追踪算法，输入字典和图像，获得图像在字典下的稀疏系数。有部分解释-OMP algorithm: matching pursuit algorithm, the input dictionary and images, access images in the dictionary under the sparse factor. Some explanation
KSVD_Matlab_ToolBox
- KSVD原始算法：信号稀疏表示中的过完备字典的学习算法-KSVD original algorithm: Signal Sparse Representation of the learning algorithm over-complete dictionary
K-SVD工具箱
- 用于信号稀疏表示的K-SVD字典学习算法工具箱，有详细的Demo，方便理解。
ompbox9
- 稀疏分解工具包，采用OMP算法，可用于稀疏字典的构造，压缩感知等领域-Sparse decomposition tool kit, using OMP algorithm, can be used for the construction of sparse dictionary, compressed sensing and other areas
ksvdbox12
- 采用KSVD算法通过训练的方法来构造稀疏过完备字典，在使用时一定要确保已装有ompbox9。可用于语音，图像信号处理等的稀疏字典构造-KSVD algorithm using the method of training to construct the sparse over-complete dictionary, in use, make sure have been installed ompbox9. Can be used for the sparse dictionary cons
gabor_function.m
- 自己写的Gabor方程函数，可以用来产生过完备的稀疏表示字典。-Gabor write their own equation function, can be used to generate the sparse representation over-complete dictionary.
CSlearning
- CS理论里观测矩阵的优化算法以及训练稀疏字典的算法-optimization of measurement matrix and dictionary learning for sparse approximations
sgLasso
- 目前比较流行的稀疏分解重构程序，可以用在人脸识别、字典构造等方面。-Currently popular sparse decomposition and reconstruction process, can be used in face recognition, dictionary structure and so on.
aairomp
- 基于压缩传感CS的经典重构算法:正交匹配追踪OMP，能很好的重构稀疏信号。-Compressed sensing based on the classic CS reconstruction algorithms: orthogonal matching pursuit OMP, the reconstruction of sparse signals is well 朗读显示对应的拉丁字符的拼音 字典 翻译以下任意网站El Confidencial-西班牙语Nord-Cine
Face-image-classification-method
- 人脸稀疏分类研究，基于DD-DT CWT多字典的人脸特征稀疏分类方法-Face thinning classification, based on DD-DT CWT dictionary feature sparse classification method
dct-dft--dwt
- 基于Matlab的压缩感知DCT、DWT、DFT正交基及过完备字典稀疏分解信号及重构-Matlab-based compression perception DCT, DWT, DFT orthogonal basis and complete dictionary signal sparse decomposition and reconstruction
CVPR-ScSPM
- 有关线性动态系统的稀疏编码和字典学习的Matlab源代码(Matlab source code about Sparse Coding and Dictionary Learning with Linear Dynamical Systems)
ksvdsbox11-min
- KSVD 算法 K-SVD通过构建字典来对数据进行稀疏表示，经常用于图像压缩、编码、分类等应用(KSVD algorithm K-SVD sparse data is represented by building dictionaries, often used for image compression, coding, classification, and other applications)
SPAMS
- 先输入数据生成相应的字典，再输入检测信号后得到用字典稀疏表示的结果(First input data, generate the corresponding dictionary, and then input the detection signal to obtain sparse dictionary results)
稀疏分解
- 信号稀疏表示的目的就是在给定的超完备字典中用尽可能少的原子来表示信号，可以获得信号更为简洁的表示方式，从而使我们更容易地获取信号中所蕴含的信息，更方便进一步对信号进行加工处理，如压缩、编码等(Signal sparse representation is to overcomplete dictionary given in as little as possible to represent atomic signal, signal can be more succinct represen
AnalysisKSVDbox
- K-SVD可以看做K-means的一种泛化形式，K-means算法总每个信号量只能用一个原子来近似表示，而K-SVD中每个信号是用多个原子的线性组合来表示的。 K-SVD通过构建字典来对数据进行稀疏表示，经常用于图像压缩、编码、分类等应用。(K-SVD can be regarded as a generalized form of K-means. The total K-means algorithm can only approximate one signal for each sem
Fisher字典学习
- 基于稀疏表示的高光谱图像分类的Fisher字典学习方法matlab代码(Hypersynthetic image classification based on sparse representation in Fisher dictionary learning matlab code.)
FDDL
- 基于Fisher字典学习的稀疏表示分类算法。(Sparse representation classification algorithm based on Fisher dictionary learning.)