搜索资源列表
SWImage
- The Swendsen-Wang Cuts algorithm is used to label atomic regions (superpixels) based on their intensity patterns using generative models in a Bayesian framework. The prior is based on areas of connected components, which provides a clean segmentation
识别图元
- 图元识别：数字化图像是一个m*m的像素矩阵。每个像素要么为0，要么为1。识别图元就是对图元像素进行标记，当且仅当两个像素属于同一图元时，他们的标号相同-map Identification : digital image is an m * m pixel matrix. Each pixel either 0 or 1. Recognition is the right map billion yuan pixel map marking, if and only if two pixels
八值图像连通区域标记
- 八值图像连通区域标记，为每个连通区域分配一个唯一的标号，处理后的图像按照从左到右，从上到下的顺序获得连续的标号,Binary image connected region eight marks, for each connected region a unique label distribution deal with in accordance with the image from left to right, from top to bottom order of access to
Read_MNIST_DataSet
- 读取MNIST数据集中图像数据文件和图像标签文件并转化成BMP图片和txt格式的标签数据。本程序简单易用，注释清楚。直接运行代码，选择相应文件即可输出BMP图片或txt文件。-The program is used to read the MNIST data set and translate the image data file and image label file into BMP or txt files.
Label a watershed image
- 图像的分水岭分割及标记分块。是进行图像处理的有利工具-Image segmentation and marking a watershed block. Is a powerful tool for image processing
imageviewer.tar
- The source code for an image viewer. It illustrated how to use pyqt4 bindings to create menu for mainwindow, how to control sidebar and how to use image label class. The CPP source for identical function is also provided.-The source code for an
growcut
- GrowCut algorithm from "GrowCut" - Interactive Multi-Label N-D Image Segmentation：可用于目标提取的分割算法，包括算法原文 -GrowCut algorithm from " GrowCut" - Interactive Multi-Label ND Image Segmentation: segmentation can be used for object extraction algori
restore
- 图像增强的目标是改进图片的质量，例如增加对比度，去掉模糊和噪声，修正几何畸变等；图像复原是在假定已知模糊或噪声的模型时，试图估计原图像的一种技术。 图像增强按所用方法可分成频率域法和空间域法。前者把图像看成一种二维信号，对其进行基于二维傅里叶变换的信号增强。采用低通滤波（即只让低频信号通过）法，可去掉图中的噪声；采用高通滤波法，则可增强边缘等高频信号，使模糊的图片变得清晰。具有代表性的空间域算法有局部求平均值法和中值滤波（取局部邻域中的中间像素值）法等，它们可用于去除或减弱噪声。 -A
randomwalk
- Multi-Label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials
label
- 图像分割处理：Roberts,Sobel,LOG,Prewitt算子处理，街区距离方法，欧几里得距离方法，边缘提取，去噪-Image segmentation: Roberts, Sobel, LOG, Prewitt operator handling, block distance method, Euclidean distance method, edge detection, noise reduction
Graph_seg
- 采用Graph Cut算法进行multiple-label的图像分割，算法很完整，非常适合初学者以及开发人员参考-multiple-label image segmentation using Graph Cut based algorithm, suitable for beginners and developers
label
- 在matlab开发环境中，对于图像采用贴便签算法识别出不同连通域后，对该图像的不同连通域索引不同的颜色，显示出标签的效果！-In the Matlab development environment for the images posted notes algorithm to identify different connected domains, the different colors of the image connected domain index, showing the e
Image-Marking
- 能够对任意图像进行打开和标注，并把标注的图像区域坐标用txt格式来保存-Able to open and annotations to any images, and put the label area of the image coordinates with TXT format to save
hand-label
- 道路场景识别，通过对样本图像处理和特征提取，再通过bp神经网络进行学习，最后通过学习后得到的权值进行样本识别。-Road scene recognition, through the sample image processing and feature extraction, and then through bp neural network learning, and finally by learning the weights obtained after the sample ide
Label-a-watershed-image
- 图像的分水岭分割及标记分块。是进行图像处理的有利工具-Image segmentation and marking a watershed block. Is a powerful tool for image processing
064
- 设置属性表组件标签图像，C++Builder精选编程学习源码，很好的参考资料。 -Settings property sheet component label image, C++Builder featured programming learning source, a good reference.
Book-label-division
- 图书标签分割，MATLAB，当你可以支付一小作业参考，可以很好地得到一个二值图像-Book label division, MATLAB, when you can pay a small job reference, you can get a good binary image
Image-Segmentation
- 该代码主要应用于图像处理中的图像分割，其目的是简化或改变图像的表示形式，使得图像更容易理解和分析。图像分割通常用于定位图像中的物体和边界（线，曲线等）。更精确的，图像分割是对图像中的每个像素加标签的一个过程，这一过程使得具有相同标签的像素具有某种共同视觉特性。-The code is mainly used in image processing in image segmentation, which aims to simplify or change the representation
shape-label
- 一种简单而快速的利用图像的轮廓在极坐标中的特征，做形状的匹配 matlab实现- a simple and fast image characteristics in polar coordinates, the outline of shape matching of matlab
label
- 对于深度学习目标分类，需要处理图像数据库，本程序能够将生成图像标签txt。(generate Image label)