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实验报告2
- 重点撑握数值微分法.此法主要内容为先算出直线的斜率 k=△y/△x 其中, △x=x1-x0, △y=y1-y0,(x0,y0)和(x1,y1)分别是直线的端点坐标。然后，从直线的起点开始，确定最佳逼近于直线的y坐标均为整数，让X从起点到终点变化，每步递增1，计算对应的y坐标，y=kx+B,并取象素（x，round(y)）。用这个方法既直观，以可行，然而效率低。-focus on shoring grip numerical differentiation. This method is mai
k_means
- K-means algorithm in C++ user pre-defined cluster number input file of data points output file of final best centroids
xitong
- 对图像进行阈值，对比度，灰度，尺寸的调整，任意角度旋转，锐化，各种滤波处理，包括中值，均值，N*N最值滤波，修复，背景清除，K均值分色，二值化，各种边缘检测，以及后续处理细化等功能。-Threshold the image, contrast, gray, size adjustment, arbitrary angle rotation, sharpening, all kinds of filtering, including the median, mean, N* N the best
kmeans
- 加强了的K-MEANS聚类函数，速度比以前快5 。适合多种聚类场合。一般聚类数为2-5之间为最佳。-The enhanced K-MEANS clustering function, speed 5 faster than before. Clustering for a variety of occasions. General the number of clusters for the best between 2-5.
KSVD_Matlab_ToolBox
- The K-SVD is a new algorithm for training dictionaries for linear representation of signals. Given a set of signals, the K-SVD tries to extract the best dictionary that can sparsely represent those signals.
KSVD_Matlab_ToolBox
- K-SVD算法应用，图像去噪，提高图像质量-The K-SVD is a new algorithm for training dictionaries for linear representation of signals. Given a set of signals, the K-SVD tries to extract the best dictionary that can sparsely represent those signals.
kriging
- 包括基本的克里金(Kriging)插值法实现代码，仅实现基本方法部分，不包含扩展克里金方法- kriging uses ordinary kriging to interpolate a variable z measured at locations with the coordinates x and y at unsampled locations xi, yi. The function requires the variable vstruct
BM3D_toolbox
- Dabov K, Foi A, Katkovnik V提出的BM3D算法的工具箱，是目前实现图像去噪效果最好的方法之一-Dabov K, Foi A, BM3D algorithm toolbox Katkovnik V presented, is currently one of the best methods of image denoising