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The aim of Optical Character Recognition (OCR) is to classify optical patterns (often contained in a digital image) corresponding to alphanumeric or other characters. The process of OCR involves several steps including segmentation, feature extractio
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基于kmeans的图像分割算法，首先得到图像，再确定分类，并计算各分类的均值，从而得到各个像素的分类。-Kmeans-based image segmentation algorithm, first of all get the image, and then determine the classification, and calculate the various categories mean, and thus obtain the classification of each pi
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基于K均值算法和互信息熵差的算法，可以有效地确定分类数,从而该算法对医学图像进行自动优化分割。-K means algorithm based on entropy and mutual information algorithms, can effectively determine the classification number, so that the algorithm for automatic optimization of medical image segmentation.
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支撑向量机的函数，可以用于识别、分类和分割，是很好的分类算法-Support Vector Machines function, can be used for identification, classification and segmentation, is a good classification algorithm
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智能运输系统中车牌识别技术得到了广泛应用 , 车牌分割是车牌识别的重要部分。基于彩色图像车牌分割与采用灰度图像车牌分割相比 , 可以有效消除阴影影响 , 同时车牌颜色也是车牌识别的一个参数。颜色分类处理使用特征函数 , 可以减少颜色坐标转换运算 , 提高颜色分类速度。文中详细讨论中国车牌特征 , 给出车牌分割详细步骤。车牌
区域判别采用信息融合技术。车牌倾斜矫正结合车牌倾斜特点 , 提出快速算法。-Intelligent Transport System in the license pla
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此源码可对图像进行Ncut分割，并且集成了特征提取和SVM分类的功能。-This source can be Ncut image segmentation, and integrates the feature extraction and SVM classification function.
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GSNAKE API provides tools for contour modeling, extraction, detection and classification, based on generalized active contour model (g-snake). GSNAKE consists of a set of objects built in C++, suitable for use in the area of feature extraction, chara
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基于广义高斯模型的自动阈值选取(GKIT)，用于图像分割、图像分类。-GKIT：Threshold Selcetion Based on the Generalized Gaussian Models，for image segmentation and classification
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基于kmeans的图像分割算法，首先得到图像，再确定分类，并计算各分类的均值，从而得到各个像素的分类。-Kmeans-based image segmentation algorithm, first of all get the image, and then determine the classification, and calculate the various categories mean, and thus obtain the classification of each pi
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指纹图像的质量测量与评价,在指纹图像分割、增强及指纹匹配等环节都有重要应用. 同时,指纹图像的质量分类,对指纹识别算法的适用性研究也有重要意义. 本文提出一种基于支持向量机的指纹图像质量分类方法.该方法选择梯度、Gabor特征、方向对比度等指标,利用支持向量机有效实现指纹图像质量分类. 并采用少类样本合成过采样技术( SMOTE)降低指纹图像质量好坏的类别不平衡问题对分类的影响. 理论分析和实验结果都表明该方法能够较为有效地提高指纹图像质量分类的正确率.-Fingerprint Image Qu
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用来对图像进行分类。Source code for Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Automatic Framework. Computer Vision and Pattern Recognition (CVPR) 2009，Li-Jia Li, Richard Socher and Li Fei-Fei. -Source code for Towards
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改进的分级多相图像分割模型及其快速实现Improved classification of multi-phase segmentation model and its rapid implementation-Improved classification of multi-phase segmentation model and its rapid implementation
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用于图像的纹理分割，适合用于图像的纹理分割和图像特征分类，并且是基于MATLAB实现的纹理特征分割。-For image texture segmentation, texture suitable for image segmentation and image feature classification, and is based on the MATLAB implementation of the texture features segmentation.
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车牌识别中，图像预处理、二值化，图像分割以及字符识别和分类的方法综述研究。并对主流方法进行了实现。-License plate recognition, image pre-processing, binarization, image segmentation and character recognition and classification Methods. And mainstream methods to achieve.
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遥感分析，一是目视解译，二是计算机信息提取，计算机提取包括自动分类、纹理识别、图像分割、还有面向对象的信息提取-Remote sensing analysis, one is visual interpretation, the two is the computer information extraction, automatic classification of computer includes extraction, texture recognition, image segmen
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描述图像分割的基本方法以及分类，并对各个方法进行了比较，对初学者很有意义-Describe the basic methods of image segmentation and classification, and compares the various methods, is very meaningful for beginners
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《MATLAB 神经网络个案例分析》，可以实现对图像进行分割，去噪，识别和分类。-" MATLAB neural network 43 case studies" can be achieved on image segmentation, de-noising, identification and classification.
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用来图像分割，特征提取，目标跟踪，分类的工具箱-Used for image segmentation, feature extraction, target tracking and classification of the toolbox
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针对手写体汉字识别问题，选取笔段和笔划作为基元，分析手写体汉字的组成规律和变形规律，提出了两种汉字结构模型：笔段中心点模型和笔划关系矩阵模型，以及基于模型的分类依据和识别方法。根据所提出的模型，采用两级分类方案构造汉字识别系统 粗分类采用笔段中心点法，细分类采用笔划关系矩阵法。-Aiming at the problem of handwritten Chinese character recognition, selecting pen and stroke as primitives, an
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用kfcm算法实现数据的聚类和分类效果。(Kfcm algorithm is used to achieve data clustering and classification results.)
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