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fdgetnew
- 将原始图象分成eXe大小的子块，找出每块中的最大值和最小值，计算差值并量化，再用rXr大小的子块计算分形值-original image will be divided into eXe size of the block to identify every piece of the maximum and minimum, margin calculations and quantified, and then rXr size of the block-value calculation F
pipei
- 图像匹配的的原图和目标图片，很实用的，欢迎下寨-image matching the maximum and objectives of the pictures, very practical and welcome Xiaqian
nwh060620
- 使用Visual C++实现同一幅较小真彩色图象在屏幕上呈（米字型8方向）圆形分布，每幅子图分别显示红、绿、兰、色调、饱和度、亮（灰）度、梯度分量图和原图。-use Visual C with a smaller achieve true color image on the screen was (H 8 meters direction) Round distribution of each figure showed the red, green, blue, hue, saturatio
EntropyThresholdingMethod
- 一个用labview编写的基于一维最大熵的图像阈值分割方法。使用方法请看Readme.txt-use a PC to prepare a peacekeeping based on maximum entropy image thresholding methods. Look at the use Readme.txt
MaximumEntropy
- 关于用二维最大熵对图像进行分割的算法文章。希望给大家提供一些参考-on two-dimensional maximum entropy of the image segmentation algorithm article. Hope to provide some reference
2223222
- 我们给出一个模板 和一幅图象 。不难发现原图中左边暗，右边亮，中间存在着一条明显的边界。进行模板操作后的结果如下： 。 可以看出，第3、4列比其他列的灰度值高很多，人眼观察时，就能发现一条很明显的亮边，其它区域都很暗，这样就起到了边沿检测的作用。 为什么会这样呢？仔细看看那个模板就明白了，它的意思是将右邻点的灰度值减左邻点的灰度值作为该点的灰度值。在灰度相近的区域内，这么做的结果使得该点的灰度值接近于0；而在边界附近，灰度值有明显的跳变，这么做的结果使得该点的灰度值很大，这样
cccccc
- 最近整理文档，发现以前写的《如何在NT下获取进程的路径》一文中还有个问题没有解决：原文中的程序无法获取系统进程的路径，如：csrss.exe。记得VCKBASE上有位网友说过一个方法：“给枚举的进程增加SE_DEBUG_NAME权限即可”，于是在网上找了些资料，解决了原文中的问题。这里要特别感谢那位名叫rovershen的网友！ GIF图象是基于颜色列表的（存储的数据是该点的颜色对应于颜色列表的索引值），最多只支持8位（256色）。GIF文件内部分成许多存储块， 用来存储多幅图象或
Kmeans.Cluster.using.Guide
- 图像集群（Image Clustering） (1)图像读入，显示图像所在路径； (2)采用imgcluster函数进行图像集群，选择集群个数后进行图像集群； (3)运行后，在原图像上显示集群灰度图； (4)若要显示各个集群情况，可打开【Show Clustering Image】新窗体，显示各集群类的基于原图的彩绘区域。其中非当前集群范围，则显示灰度为255的黑色。用户可点击按纽上下查看所有集群图。-image cluster (Image Clustering) (1) re
CannyJava
- 图像边缘检测(Canny 算法)的Java实现 Canny边缘检测算法 step1:用高斯滤波器平滑图象； step2:用一阶偏导的有限差分来计算梯度的幅值和方向； step3:对梯度幅值进行非极大值抑制； step4:用双阈值算法检测和连接边缘 -Edge Detection (Canny algorithm) to the Java Canny edge detection algorithm ste p1 : Gaussian smoothing filte
200608121531
- 边缘检测:直接运行程序时，首先打开原图并点击左视图，使之显示原图像；然后点击右视图并选择菜单中的处理函数，处理后的图像即会在右视图上显示-Edge Detection : Direct operating procedures, the first to open the maximum and click Left Side, so that it shows the original image; Then click View and choose the right menu proce
zuidashang1
- 图像分割：最大熵对灰度图像基于区域的阈值分割-image segmentation : Maximum Entropy gray images on the threshold based on regional segmentation
thresh_md_OTSUhy
- 自动地otsu 二值图像分割方法，该方法也叫 基于最大类间方差地 图像二值分割方法，希望大家喜欢-otsu two automatic image segmentation methods, The method is also called for the maximum variance to image two segmentation methods, hope you like
hyperspectral
- 用于高光谱图像处理的matlab程序，包括图像融合，降维和图像的极大似然分类程序-for hyperspectral image processing Matlab procedures, including image fusion, dimensionality reduction and image of the maximum likelihood classification procedures
hdjm
- 抽取子图像: 通过一个随机混沌标签序列从原图像中抽取 对子图像加密:小波变换 ,在高频部分加密 重构原图，把加密好的子图像重新放回到原图像中 -from sub-images : a random chaotic sequence label from the original image from a tradition image encryption : wavelet transform, the high-frequency part of remodeling the
Multi-CameraCalibration
- A one-dimensional calibration object consists of three or more collinear points with known relative positions. It is generally believed that a camera can be calibrated only when a 1D calibration object is in planar motion or rotates around a &mac
thresh_md
- 图像分割，实现最大实现最大方差法计算分割门限-image segmentation, the highest maximum variance method segmentation Threshold
vi2
- This program compress and recostruct using wavelets. We can select level of decomposition(here maximum 4 levels are given) of images using selected wavelet. For eg:-wavelets can be haar, db1, db2,dmey............... Decomposition can be viewed in
HistogramVC
- // // Histogram Sample // This sample shows how to use the Sample Grabber filter for video image processing. // Conceptual background: // A histogram is just a frequency count of every pixel value in the image. // There are various well-
imageindex
- 图像匹配的的原图和目标图片，很实用的，欢迎下寨-image matching the maximum and objectives of the pictures, very practical and welcome Xiaqian
snake.m
- snakes are a variational technique to find the best contour that aligns with an image. The basic routine, snake.m, aligns a sequence of points along a contour to the maximum of an array or image. Provide it with an image, a set of starting points, li