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伯克利大学开发基于Unix，Linux环境下的图像分割评价打分程序-developed at the University of Berkeley-based Unix, Linux environment evaluation of image segmentation scoring procedures
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一本系统介绍图像分割和评价的书，对学习图像分割的人来说，非常好用-Introduced a system of image segmentation and evaluation of books for learning image segmentation of people, very easy to use
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图像分割，具体算法参考一下论文
Zhi-Gang Tan， Xiao-Chen Heand Nelson H. C. Yung
A Novel Merging Criterion Incorporating Boundary Smoothness and Region Homogeneity for Image Segmentation-Abstract
A novel joint region merging criterion combining region ho
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可以检测图像中圆和直线的信息，有利于初学者使用学习。-Edge detection has played an important role in the field of computer vision. A parametric edge detection method based on recursive mean-separate image decomposition is introduced. A method for automatic parameter selection
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该文章总结了近几年来一些经典的图像分割算法，并对他们的效果进行了评测。 从中可以了解该领域动向，对调研工作有很大帮助。-The article summarizes recent years a number of classical image segmentation algorithm, and the evaluation of their effectiveness. From this we can understand the trends in the field of rese
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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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压缩文件里有四种图像分割的算法源代码，即阈值法、区域增长法、分裂合并法和K均值法。图片可用于检验。-The rar folder includes four source code of image segmentation,ie.thresholding, region growing, splitting and merging, kmeans. The images are able to be used for evaluation and verification.
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本文主要介绍了图像分割的两大类方法基于边缘的分割方法和基于区域的分割方法及其各自存在的问题和最新进展同时简单介绍了物理的分割方法和图像分割评价最后指出了图像分割技术的发展趋势-In this paper, two types of image segmentation method segmentation method based on the edge of the region-based segmentation method and their respective problems
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伯克利图像小组提出来的图像分割评价指标：边界误差。-Evaluation of image segmentation. Image: Berkeley group boundary error.
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Contour Detection and Hierarchical Image Segmentation (UC Berkeley)
MATLAB/C++混编
Arbela?ez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour Detection and Hierarchical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine
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用于对分割后的图片质量进行评价，适用于初学者，希望对你有帮助(For the quality of the image after the evaluation of the segmentation, suitable for beginners, I hope to help you)
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医学图像分割方法的研究较多,而对分割算法的评价方法的研究却很少的问题,提出了一种判断和比较医学图像分割算法优劣的评价方法。首先对现有的几种评价方法进行了综述,并总结出了一套评价系统。可靠性、精确性、区域统计特性和效率是评价一个分割方法的4个重要参数(There are many researches on medical image segmentation methods, but there are few researches on the evaluation methods of se
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