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matlab-goat

于 2021-05-13 发布 文件大小:9615KB
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说明：  MATLAB遗传算法图像边缘检测和图像分割(MATLAB genetic algorithm image edge detection and image segmentation)

文件列表：

遗传算法图像分割matlab%2B源代码
.............................\一种基于量子遗传算法的红外图像分割方法.pdf,331966,2008-04-15
.............................\一种自适应的多目标图像分割方法.pdf,367247,2008-04-15
.............................\图像分割新方法综述.pdf,254353,2008-04-16
.............................\图像阈值分割算法实用技术研究与比较.pdf,377081,2008-04-16
.............................\基于MATLAB的遗传算法的源程序
.............................\............................\GAOT
.............................\............................\....\arithXover.m,1490,1996-02-06
.............................\............................\....\b2f.m,1495,1996-02-06
.............................\............................\....\binaryMutation.m,1507,1996-02-06
.............................\............................\....\boundaryMutation.m,1642,1996-02-06
.............................\............................\....\calcbits.m,1382,1996-02-06
.............................\............................\....\Contents.m,3021,1996-02-06
.............................\............................\....\coranaEval.m,1451,1996-02-06
.............................\............................\....\coranaMin.m,1222,1996-02-06
.............................\............................\....\delta.m,1471,1996-02-06
.............................\............................\....\f2b.m,1492,1996-02-06
.............................\............................\....\ga.m,10718,1996-02-06
.............................\............................\....\gademo1.m,4830,1996-02-06
.............................\............................\....\gademo1eval1.m,1274,1996-02-06
.............................\............................\....\gademo2.m,2819,1996-02-06
.............................\............................\....\gademo3.m,6261,1996-02-06
.............................\............................\....\gaot.dvi,57780,1995-08-30
.............................\............................\....\gaot.ps,133622,1996-01-16
.............................\............................\....\gaotindex.html,3317,1996-02-07
.............................\............................\....\heuristicXover.m,2139,1996-02-06
.............................\............................\....\index.html,2600,1996-02-07
.............................\............................\....\initialize.m,3199,1996-02-06
.............................\............................\....\maxGenTerm.m,1265,1996-02-06
.............................\............................\....\multiNonUnifMutation.m,1991,1996-02-06
.............................\............................\....\nonUnifMutation.m,2193,1996-02-06
.............................\............................\....\normGeomSelect.m,2311,1996-02-06
.............................\............................\....\optMaxGenTerm.m,1421,1996-02-06
.............................\............................\....\parse.m,1459,1996-02-06
.............................\............................\....\htm" target=_blank>README,803,1996-02-07
.............................\............................\....\roulette.m,1781,1996-02-06
.............................\............................\....\simpleXover.m,1614,1996-02-06
.............................\............................\....\tournSelect.m,1619,1996-02-06
.............................\............................\....\unifMutation.m,1651,1996-02-06
.............................\基于二维最大熵和改进的遗传算法的图像分割.pdf,441740,2008-04-15
.............................\基于免疫算法的图像阈值分割.pdf,335850,2008-04-15
.............................\基于改进遗传算法的图像分割.pdf,202314,2008-04-16
.............................\基于混沌遗传算法的图像阈值分割.pdf,303862,2008-04-15
.............................\基于遗传算法的Otsu法在图像分割中的应用(1).pdf,1455097,2008-04-16
.............................\基于遗传算法的Otsu法在图像分割中的应用.pdf,1455097,2008-04-15
.............................\基于遗传算法的二维最小交叉熵的动态图像分割.pdf,632881,2008-04-15
.............................\基于遗传算法的模糊熵多阈值图像分割.pdf,222992,2008-04-15
.............................\基于遗传算法的聚类分析在CT图像分割中的应用.pdf,614084,2008-04-16
.............................\基于遗传算法的自适应最优阈值图像分割技术研究.pdf,264953,2008-04-15
.............................\基于遗传算法的自适应聚类图像阈值分割方法.pdf,436550,2008-04-15
.............................\基于遗传算法的阈值图像分割研究(1).pdf,290737,2008-04-16
.............................\基于遗传算法的阈值图像分割研究.pdf,290737,2008-04-15
.............................\基于遗传量子的自适应图像分割算法.pdf,209599,2008-04-15
.............................\基于量子遗传算法的二维最大熵图像分割.pdf,297372,2008-04-16
.............................\很像!!基于改进遗传算法的图像分割方法.pdf,359977,2008-04-15
.............................\用matlab做边缘提取的代码
.............................\........................\edgedetect_basedonWavelet.m,5119,2006-04-07

.............................\用遗传_神经网络方法进行图像分割的研究.pdf,299632,2008-04-15
.............................\遗传算法在图像处理中的应用.pdf,459484,2008-04-10
.............................\遗传算法的最佳熵在图像分割中的应用.pdf,259653,2008-04-15
.............................\采用遗传算法与最大模糊熵的双阈值图像分割.pdf,472189,2008-04-16

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    EDLines是一种快速直线检测算子，2012年在ICCV上提出，也是目前处理直线检测最快的算法之一，该算法包含三个步骤：（1）边缘提取：利用Edge Drawing (ED)算法[28,29]从灰度图像中提取边缘片段；（2）线段检测：利用最小二乘法提取直线段；（3）线段确认：遵循Helmholtz定律，从已提取的直线段中摒弃虚假线段。EDLines算法的优越性得益于Edge Drawing (ED)算法能够从灰度图像中准确、快速、稳定地提取出光滑、完整的边缘片段。Edge Drawing (ED)算法包含以下步骤： （1）采用size=5*5,σ=1的高斯核对灰度图像进行平滑滤波，去除噪声； （2）采用一种常用的梯度算子，如Prewitt、Sobel或Scharr等计算平滑后图像中每个像素点的梯度幅度和梯度方向； （3）将梯度图中邻域内幅度最大的像素点标记为锚点，这些锚点成为图像边缘点(edge elements)的概率很大； （4）将相邻的锚点连接成边缘线。从一个锚点起始，ED利用相邻像素的梯度幅度和方向在梯度为最大值的锚点之间游走。 (Edge Drawing (ED) is our recently-proposed, novel, fast edge detection algorithm. What makes ED stand out the existing edge detectors, e.g., Canny, is the following: While the other edge detectors give out a binary edge image as output, where the etected edge pixels are usually independent, discontinuous entities ED produces a set of edge segments, which are clean, contiguous, i.e., connected, chains of edge pixels. Thus, while the output of the other edge detectors requires urther processing to generate potential object boundaries, which may not even be possible or result in inaccuracies ED not only produces perfectly connected object boundaries by default, but it also achieves this in blazing speed compared to other edge detector.)
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