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[Special Effects] texture3 DL : 0: 本程序在对图像进行纹理分析（基于共发矩阵的方法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (based on total fat matrix method), on the basis of access to different regions of the image texture features, these features texture, using clustering (K-mean) the right image classification algorithm for a regional breakdown!
Date : 2008-10-13 Size : 342.44kb User : 陈镇静
[Special Effects] textureA2 DL : 0: 本程序在对图像进行纹理分析（由于共发矩阵的方法效果很不好，本程序采用基于频率域的纹理分析算法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (due to a total of hair matrix, the effect is very bad, the program uses a frequency domain based on the texture analysis algorithm), on the basis of access to different regions of the image texture features, these texture characteristics, using clustering (K-mean) the right image classification algorithm for a regional breakdown!
Date : 2008-10-13 Size : 296.51kb User : 陈镇静
[Special Effects] texture3 DL : 0: 本程序在对图像进行纹理分析（基于共发矩阵的方法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (based on total fat matrix method), on the basis of access to different regions of the image texture features, these features texture, using clustering (K-mean) the right image classification algorithm for a regional breakdown!
Date : 2025-12-29 Size : 342kb User : 陈镇静
[Special Effects] textureA2 DL : 0: 本程序在对图像进行纹理分析（由于共发矩阵的方法效果很不好，本程序采用基于频率域的纹理分析算法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (due to a total of hair matrix, the effect is very bad, the program uses a frequency domain based on the texture analysis algorithm), on the basis of access to different regions of the image texture features, these texture characteristics, using clustering (K-mean) the right image classification algorithm for a regional breakdown!
Date : 2025-12-29 Size : 296kb User : 陈镇静
[Special Effects] TextureAnlysis DL : 0: TextureAnlysis.m实现遥感图像的纹理分析，以方向邻域内的灰度均值和灰度共生矩阵的熵作为纹理特征，使用k-means聚类。-TextureAnlysis.m the realization of remote sensing image texture analysis to the direction of the gray-scale neighborhood of the mean and the entropy of gray level co-occurrence matrix as texture features, the use of k-means clustering.
Date : 2025-12-29 Size : 203kb User : xxl
[Special Effects] kmeans1 DL : 0: k-均值聚类算法实现灰度图像分割，输入图像矩阵和聚类中心个数，返回为最终的聚类中心和图像中每个像素所属类的编号（对应于图像矩阵）-k-means clustering algorithm to achieve gray-scale image segmentation, the input image matrix and the number of cluster centers, the return for the final image of the cluster centers and their respective categories in each pixel number (corresponding to the image matrix)
Date : 2025-12-29 Size : 1kb User : cc
[Special Effects] kmean DL : 0: 基于OpenCV的二元吗，实现了划分随机分布点集的K-均值算法。按照类别分组的输入样本顺序输出包含样本类别索引的数组labels(i)，存储在样本矩阵的第i行中。-Based on the binary OpenCV吗, implementation of the demarcation point set of randomly distributed K-means algorithm. Input in accordance with the type of packet contains samples of the output samples of the order of the array index type of labels (i), stored in the sample matrix of the first i rows.
Date : 2025-12-29 Size : 5kb User : 无梦
[Special Effects] 51622445texturepinyuA2 DL : 0: K均值聚类算法由于对纹理图像使用灰度共生矩阵分割效果不明显因此该算法使用图像频域进行处理-K-means clustering algorithm because of the texture image segmentation using the gray co-occurrence matrix effect was not obvious, therefore use the algorithm for processing images in frequency domain
Date : 2025-12-29 Size : 297kb User : 小五子
[Special Effects] hw2 DL : 0: 1.对一个256*256的图像进行DCT变换得到图像D，将D得斜下角数值置为零，然后进行DCT反变换. 2.对源图像进行K-L转换 1和2比较-1.Get a grey level image which size is N*N. (For example, 256*256, however, N = ), and partition to 8*8 sub images. 2.. Apply DCT to these sub images, and get the transformed image D with DCT coefficients for elements. 3. From D, keep the coefficient values for only upper left triangular region and set zeros for lower right region to approximate the image. (That is, only half of data is used.) 4.Take Inverse DCT to get the approximated image. 2 . Get the covariance matrix of image. 3 . Calculate the corresponding eigenvectors and eigenvalues. 4 . Represent the original image with Singular Value Decomposition. 5 . Approximate the image by taking off the 4 smallest eigenvalues. (That is, only half of information is used.)
Date : 2025-12-29 Size : 1kb User : zhengyan
[Special Effects] ImageRetrieval DL : 0: 毕业设计，基于内容的图像检索，支持的检索特征包括 sift,颜色直方图，灰度矩阵，HU不变矩，边缘方向直方图，检索方法使用K-means和K-D树两种，需要OPENCV支持，运行时请先选定一个文件夹来生成特征库，特征库用access数据库保存，只支持JPG文件-Graduate design, content-based image retrieval, search features, including support sift, color histogram, gray matrix, HU moment invariants, edge direction histogram, retrieval method using the K-means and KD trees are two kinds of needs OPENCV support Please select a runtime folder to generate the feature library, feature library with access database save, only supports JPG files
Date : 2025-12-29 Size : 351kb User : 平天羽
[Special Effects] main DL : 0: 将样本矩阵FaceContainer进行主成分分析的整个过程封装在main函数中，参数K是主分量数目，即降维至K维。计算得出样本矩阵的低维表示LowDimFacesitting和主成分分量矩阵W。-The sample matrix FaceContainer principal component analysis of the whole process is encapsulated in the main function, the parameter K is the number of principal components, namely dimension reduction to K-dimensional. Calculated low-dimensional representation of the sample matrix and principal component LowDimFacesitting weight matrix W.
Date : 2025-12-29 Size : 1kb User : 刘丕玉
[Special Effects] local-statistics-toolbox DL : 0: FUNCTIONS : STATISTICS - MODA Mode of a distribution - ARGMAX calculates position of the maximum value of matrix V - ARGMIN calculates position of the minimum value of matrix V - DRAW_HIST estimates and draws histogram of data - Random k data generator 2D LOCAL OPERATORS - LOCALMEAN local mean of 2D image - VARLOCAL local variance of 2D image - CVLOCAL Local Square Coefficient of variation - LOCALMAD local Median Absolute Deviation (MAD) - FUNCTIONS : STATISTICS - MODA Mode of a distribution - ARGMAX calculates position of the maximum value of matrix V - ARGMIN calculates position of the minimum value of matrix V - DRAW_HIST estimates and draws histogram of data - Random k data generator 2D LOCAL OPERATORS - LOCALMEAN local mean of 2D image - VARLOCAL local variance of 2D image - CVLOCAL Local Square Coefficient of variation - LOCALMAD local Median Absolute Deviation (MAD)
Date : 2025-12-29 Size : 18kb User : ANN
[Special Effects] face-detection-for-K-L-transform DL : 0: 基于K-L变换的人脸识别技术主要思想是：提取输入人脸图像矩阵的特征向量，并与图像数据库中样本特征的向量求欧氏距离，距离小于阈值时便认为识别成功。本程序即为利用K-L原理实现人脸检测的实例。-The main idea of the face recognition technology based on KL transform is: extract the matrix of the input face image feature vectors and Euclidean distance, the distance is less than the threshold sample feature vector image database and thinks that the recognition success. The program is the use of the KL the principle face detection instance.
Date : 2025-12-29 Size : 4kb User : guocheng
[Special Effects] FCM DL : 0: 自己实现的，模糊C均值聚类的代码，在K-mean上添加了隶属度矩阵。注释详细，方便理解算法步骤-my implementation, Fuzzy C-Means clustering code in the K-mean adding a membership matrix. Notes detailed, easy to understand algorithm steps
Date : 2025-12-29 Size : 30kb User : yaoli
[Special Effects] affintpoints DL : 0: 仿射不变Harris, Laplacian, det(Hessian) and Ridge 特征点检测参考文献：An affine invariant interest point detector , K.Mikolajczyk and C.Schmid, ECCV 02, pp.I:128-142.-Matlab code for detecting Affine spatial interest points. Includes Harris, Laplacian, det(Hessian) and Ridge interest point operators in combination with spatial scale selection based on the extrema of scale-normalized Laplacian and affine adaptation basen on second-moment matrix. Scale and shape adaptation are optional and disjoint. Zip archive: affintpoints.zip Ref: An affine invariant interest point detector , K.Mikolajczyk and C.Schmid, ECCV 02, pp.I:128-142. What is in the package: 1) ineterst point detection of different kinds (Harris, Laplace, det(H), Ridge) 2) scale, shape and position adaptation procedure 3) demo examples and a script for batch-mode computation and saving of the results what is not in the package: - no rotation estimation - no region descriptor computation
Date : 2025-12-29 Size : 880kb User : 灵台斜月
[Special Effects] NMF-NMF-SQ-Hashing DL : 0: 通过强大和安全的形象散列非负矩阵因式分解，在信息取证与安全，376-390，2007年IEEE交易。NMF-NMF-SQ哈希- V. Monga, and M. K. Mihcak, Robust and secure image hashing via non-negative matrix factorizations, IEEE Transactions on Information Forensics and Security, 2(3), 376-390, 2007. NMF-NMF-SQ Hashing
Date : 2025-12-29 Size : 221kb User : Dr_wong
[Special Effects] MR_K1 DL : 0: 实验三：MR图像与K空间数据关系实验（设计型）一、实验目的及要求：用MATLAB探究MR图像与K空间数据之间的关系。具体要求如下： 1．由提供的MR图像得到对应的K空间数据并显示K空间数据幅度谱； 2．隔行删除K空间数据并显示其幅度谱，由隔行删除后的K空间数据重建MR图像，观察图像的改变。 3．隔列删除K空间数据并显示其幅度谱，由隔列删除后的K空间数据重建MR图像，观察图像的改变。 4．隔行、隔列删除K空间数据并显示其幅度谱，由隔行、隔列删除后K空间数据重建MR图像，观察图像的改变。 5．通过矩阵操作语句删除K空间高频数据（或设计一个低通滤波器滤除高频数据）并显示其幅度谱，由处理后的K空间数据重建MR图像，观察图像的改变。 6．通过矩阵操作语句删除K空间低频数据（或设计一个高通滤波器滤除低频数据）并显示其幅度谱，由处理后的K空间数据重建MR图像，观察图像的改变。-Experiment 3: MR images and the K space data relationship experiment (design) A, experiment purpose and requirements: MATLAB to explore the MR images and the relationship between the K space data. Specific requirements are as follows: 1. By providing the MR images of spatial data and display the corresponding K K space data amplitude spectrum 2. Delete interlaced K space data and displays its amplitude spectrum, after deleting the interlaced K space data reconstruction of MR images, to observe the change of the image. 3. Delete every column K space data and display its amplitude spectrum, by every column K space data reconstruction of MR images after deletion, observe the change of the image. 4. Interlaced, every column deletion K space data and displays its amplitude spectrum, by interlaced, after deleting every column K space data reconstruction of MR images, to observe the change of the image. 5. By matrix operations statement to remove high ? K space
Date : 2025-12-29 Size : 3kb User :
[Special Effects] bayesgauss DL : 0: BAYESGAUSS贝叶斯分类器对高斯模式。 D = BAYESGAUSS(X,钙、马、P)计算贝叶斯决策 n维的功能模式的行X。 n-by-n-by-W大小的CA是一个数组,其中包含了协方差的矩阵大小n-by-n,W类的数量。大小n-by-W 马是一个数组,其列。柯尔- 水洼意味着向量。一个赔偿。矩阵和平均向量必须为每个类指定,即使一些都是平等的。X是大小 K-by-n,K是模式的数量分类。P是 1-by-W数组,包含发生的概率每个类。如果P不包括在参数、类假定同样可能。-BAYESGAUSS Bayes classifier for Gaussian patterns. D = BAYESGAUSS(X, CA, MA, P) computes the Bayes decision functions of the n-dimensional patterns in the rows of X. CA is an array of size n-by-n-by-W containing W covariance matrices of size n-by-n, where W is the number of classes. MA is an array of size n-by-W, whose columns are the corres- ponding mean vectors. A cov. matrix and a mean vector must be specified for each class, even if some are equal. X is of size K-by-n, where K is the number of patterns to be classified. P is a 1-by-W array, containing the probabilities of occurrence of each class. If P is not included in the argument, the classes are assumed to be equally likely.
Date : 2025-12-29 Size : 2kb User : 刘晓丹
[Special Effects] GoLPP DL : 1: D MANUSC Graph-optimized Locality Preserving Projections (GoLPP) algorithm [28]. GoLPP integrated the graph construction and a specific dimensionality reduction process (i.e. LPP) into a unified framework, which results in a simultaneous learning for optimal graph and projection matrix. From the experimental results in [20], it was demonstrated that the GoLPP outperformed the classical LPP which is based on k nearest neighbor - Graph-optimized Locality Preserving Projections (GoLPP) algorithm [28]. GoLPP integrated the graph construction and a specific dimensionality reduction process (i.e. LPP) into a unified framework, which results in a simultaneous learning for optimal graph and projection matrix. From the experimental results in [20], it was demonstrated that the GoLPP outperformed the classical LPP which is based on k nearest neighbor
Date : 2025-12-29 Size : 1kb User : 骕骦
[Special Effects] Cloud-detection- DL : 0: 首先使用灰度共生矩阵提取图像纹理特征，之后使用灰度共生矩阵的熵值与相关性系数作为纹理参数，用k-means聚类算法实现遥感图像的云检测-First of all, the gray co-occurrence matrix is used to extract the image texture features, then the entropy and correlation coefficients of the GLCM are used as texture parameters, and the K-means clustering algorithm is used to detect the cloud of remote sensing images
Date : 2025-12-29 Size : 4kb User : yunqpqg