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nteractive Segmentation using Graph Cuts
CS 578 Medical Image Analysis Final Project Report
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图像交互是分割 graph cut,实现交互式分割，并且可以连续分割-Interactive image is split graph cut, to achieve interactive segmentation, and can be continuously split
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GRABCUTOpenCV中的GrabCut算法是依据《"GrabCut" - Interactive Foreground Extraction using Iterated Graph Cuts》这篇文章来实现的。该算法利用了图像中的纹理（颜色）信息和边界（反差）信息，只要少量的用户交互操作即可得到比较好的分割结果。如果前景和背景之间的颜色反差不大，分割的效果不好；不过，这种情况下允许手工标记一些前景或背景区域，这样能得到较好的结果。-GRABCUTOpenCV in GrabCut alg
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grab_cut,graph gut的进阶版，运用了lazy snapping-There are advanced segmentation algorithms in the literature which extends the concepts of graph cuts. Most prominent of them are Graph cuts and Lazy Snapping. Lazy napping is an interactive image cut out tool.
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Connected-component labeling (alternatively connected-component analysis, blob extraction, region labeling, blob discovery, or region extraction) is an algorithmic application of graph theory, where subsets of connected components are uniquely labele
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马尔科夫图切代码，对图像处理很有用，特别是图像分割，目标识别-markov graph cut,it is useful to image processing，especially in segmentation，object recognition
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Image processing tool - segmentation:
3-D graph cut
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Image processing tool for segmentation GUI:
3-d graph cut
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基于图理论动态规划算法的OCT图像边界分割算法，边界提取效果好(OCT image boundary segmentation algorithm based on graph theory and dynamic programming algorithm, the boundary extraction effect is good)
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