搜索资源列表
seg_twoseeds
- 区域生长算法分割脑部图像， 两个种子点，解决完整分割白质的算法
区域生长法分割彩色图像
- 采用区域生长方法分割彩色图像。用matlab编写的源程序。先用鼠标获取生长点，程序完成自动分割。
regionGrow
- 该程序用于实现图像分割中的区域生长法。共有4个参数。1，2，种子点的座标，3，图像，4，生长时用到的域值。-this code is used for region growing in the image segmentation domain.
seg_oneseed
- 本程序时应用MATLAB分割技术中的区域生长来实现图像分割-Application of MATLAB in this program in the region growing segmentation technique to achieve image segmentation
interactive-segmentation-method
- 基于图像区域生长的方法之一 交互式分割法-Image-based region growing methods: interactive segmentation method
Region-growing-algorithm-
- 基于matlab的区域生长算法，可以对图像进行分割处理。-Matlab-based region growing algorithm to the image segmentation.
regionGrow
- 区域生长算法，运行后鼠标左键单击图像区域的一点开始分割，可点击多次，单击右键结束运行。-Regional growth, and after the left mouse button click on image of a division, but it starts at times, click the right key to an end run.
Matlabregiongrow
- Matlab边缘检测和区域生长图像分割算法代码，很有用的代码。-Matlab edge detection and region growing image segmentation algorithm code, useful code.
Gonzales-image-processing
- 该算法是经典的区域生长算法，在图像分割中比较常用。代码相对比较简单，也较易理解。-This algorithm is a classical region growing algorithm, and is commonly used in image segmentation.. The code is relatively simple and understandable..
5
- 利用相位一致性提取图像边缘，K-means聚类后区域生长进行图像分割，附参考论文资料。(The image edge is extracted by phase consistency, and the region growth is segmented after K-means clustering, and the reference papers are attached.)
6
- 利用相位一致性提取图像边缘，K-means聚类后区域生长进行图像分割，附参考论文资料和相关解释。(The image edge is extracted by phase consistency, and the region growth is segmented after K-means clustering, and the reference papers and relevant explanations are attached.)
1
- 利用相位一致性提取图像边缘，K-means聚类后区域生长进行图像分割，附参考论文资料和相关解释。多目标优化 一个含有两个优化目标的多目标优化问题(The image edge is extracted by phase consistency, and the region growth is segmented after K-means clustering, and the reference papers and relevant explanations are attached. M
2p
- 利用相位一致性提取图像边缘，K-means聚类后区域生长进行图像分割，附参考论文资料和相关解释。多目标优化 含有两个优化目标的多目标优化问题(The image edge is extracted by phase consistency, and the region growth is segmented after K-means clustering, and the reference papers and relevant explanations are attached. Mul