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基于matlab实现的直方图阈值图像分割算法实现源码-Matlab implementation of the histogram-based threshold image segmentation algorithm source code
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基于二维直方图的图像分割算法研究 基于二维直方图的图像分割算法研究-Two-dimensional histogram-based image segmentation algorithm based on two-dimensional histogram of the image segmentation research is based on two-dimensional histogram of the image segmentation algorithm
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image segmentation algorithm matlab source, it contains a large number of classic source code can display the image histogram of the distribution of gray, like using matlab A few people one-dimensional input and one-dimensional output. Approximation
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此代码为基于二维直方图的模糊聚类图象分割方法-This code is a two-dimensional histogram-based fuzzy clustering image segmentation method
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Matlab图像处理基础，读取图像、灰度化、画灰度直方图、迭代法实现自动阈值分割-Matlab image processing based on the read image, gray, painted gray histogram, iterative method to achieve the automatic threshold segmentation
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利用该今年遗传算法和直方图信息进行图像分割-Using the genetic algorithm this year and histogram information for image segmentation
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讨论了在对图像进行分割时的一维、二维灰度直方图分割方法, 对最优阈值灰度分割方法原理进行了描述, 得出了最优阈值灰度分割算法。-Discussed the image segmentation of the one dimensional, 2-d gray-level histogram image segmentation method, the optimal threshold gray segmentation method described the principle, it is
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本文介绍了两种图像区域分割方法，讨论了关于分割时最佳闽值选择的问题，除利用直方图确定阈值外，还介绍了利用方差确定阈值的途径。
关键词：图-This paper introduces two kind of image area segmentation method, talked about the best value selection fujian segmentation, besides the histogram threshold value determined outsi
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包含各种经典图像处理方法的Visual C源程序。图图像平滑、图像增强、直方图处理、图像分割和形态学处理等。
-Contains a variety of classical methods of image processing Visual C source code. Image smoothhing, image enhancement, histogram processing, image segmentation and morphological processing.
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运用C实现了数字图像的一些基本算法，包括canny edge detection,阈值变换，维纳滤波，直方图均衡，图像细化，旋转，图像配准，图像分割kmeans, isodata, fuzzy c means, fuzzy clustering, mrf image seg, 图像浏览，road svm using matlab, snakes matlab, anistropic gaussian filter-Canny edge detection, threshold transform
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数字图像处理的一般算法，灰度直方图，边缘检测及图像分割-General algorithm for digital image processing, histogram, edge detection and image segmentation
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赵春江编写使用C#进行数字图像处理。对图像进行分割，增强，绘制直方图，图像识别等。-Zhao Chunjiang written using C# for digital image processing. Image segmentation, enhancement, draw a histogram, image recognition.
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数字视频处理光流方程的实现，并绘制运动场，统计直方图，mesh图，并进行运动分割-Digital video processing to achieve the optical flow equation, and draw the playground, the histogram, the mesh in Figure, and motion segmentation
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程序实现了基于最大熵准则的图像分割。其中实现灰度阈值算法即算出双峰的谷底值的方法为：利用灰度直方图求谷底，然后求第一个峰值，第二个，再求谷的位置-Program to achieve the image segmentation based on maximum entropy criterion. The grayscale threshold algorithm, which is calculated bimodal trough value as follows: gray histog
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申铉京等提出三维直方图重建和降维的Otsu阈值分割算法。通过重建三维直方图，减弱了噪声干扰，将三维直方图区域划分由八分法改为二分法，使得阈值搜索的空间维度从三维降低到一维。-Shin Hyun Beijing and proposed a three-dimensional histogram reconstruction and dimensionality reduction of the Otsu threshold segmentation algorithm. Weakened by
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魏巍等人提出了一种基于灰度、邻域均值和邻域加权中值三维直方图的最大Renyi熵阈值分割算法。该方法无论在精度上还是抗噪性上都有明显提升。-Wei Wei, who based on gray-scale, neighborhood average and in the neighborhood weighted value of three-dimensional histogram of the maximum Renyi entropy threshold segmentation algo
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利用二维属性直方图的最大类间平均离差的图像分割方法-Using two-dimensional Histogram Maximum average deviation between the methods of image segmentation
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算法通过将二维直方图分为四部分：背哥、目标．受噪声干扰的背景和受噪声干扰的目标，以选取这四部分的信息熵的和最大作为闽值的选取准则。访方法有三个优点：尽可能包括背罱部分和目标部分；可以有效地提高对噪声敷据的鲁棒性；不过度地引入噪声和边缘信息。实验结果表明，谊方法具有较好的图像分割效果。-Through two-dimensional histogram algorithm will be divided into four parts: back elder brother, target. By
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使用Opencv和MFC结合，实现对图像中各个分量的分离 并绘制其颜色直方图，便于使用者估计图像分割的阈值，值得使用-To use Opencv combined and MFC achieve the respective components of the image separation and draw its color histogram, to facilitate the user to estimate the threshold value of the image segm
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利用二维属性直方图的Otsu自动阈值分割方法 提出了二维属性直方图的概概念 ,进而提出了一种基于二维属性直方图的图像自动阈值化方法 有利于写论文
-The use of the histogram properties of two-dimensional Otsu automatic threshold segmentation method takes the concept of a two-dimensional histogram attributes, and then prop
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