文件名称:segment_ga
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 323kb
- 下载次数：
- 0次
- 提 供 者：
- s***
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
下载
别用迅雷、360浏览器下载。
如迅雷强制弹出，可右键点击选“另存为”。
失败请重下，重下不扣分。
如迅雷强制弹出，可右键点击选“另存为”。
失败请重下，重下不扣分。
介绍说明－－下载内容均来自于网络，请自行研究使用
遗传算法在图像分割中的应用，有在一维和二维最大熵分割中的基本算法的实现及改进后的实现，自己看看吧。-Genetic algorithm in image segmentation, there is at one and two dimensional maximum entropy partitioning in the realization of the basic algorithms and improved to achieve their own look.相关搜索： segment_ga
遗传算法
二维最大熵
遗传算法
二维最大熵
(系统自动生成,下载前可以参看下载内容)
下载文件列表
segment_ga\2d_ksw_qiongju.asv
..........\2d_ksw_qiongju.m
..........\cross.asv
..........\cross.m
..........\cross1.asv
..........\cross1.m
..........\cross_2d.asv
..........\cross_2d.m
..........\cross_2d1.m
..........\ga_main.asv
..........\ga_main.m
..........\hist2.m
..........\ksw.asv
..........\ksw.m
..........\ksw_2d.asv
..........\ksw_2d.m
..........\ksw_2d_ga.asv
..........\ksw_2d_ga.m
..........\ksw_2d_ga_improve.asv
..........\ksw_2d_ga_improve.m
..........\ksw_2d_qiongju.m
..........\ksw_ga.asv
..........\ksw_ga.m
..........\ksw_ga_improve.asv
..........\ksw_ga_improve.m
..........\ksw_qiongju.asv
..........\ksw_qiongju.m
..........\Lenna.bmp
..........\Lenna_noise.bmp
..........\mutation.m
..........\mutation1.m
..........\mutation_2d.asv
..........\mutation_2d.m
..........\mutation_2d1.m
..........\otosu.asv
..........\otosu.m
..........\rice.tif
..........\rice_noise.tif
..........\rice_noise1.tif
..........\select.asv
..........\select.m
..........\select1.asv
..........\select1.m
..........\select_2d.asv
..........\select_2d.m
..........\select_2d1.asv
..........\select_2d1.m
..........\sourseimage.m
..........\threshold_ksw.asv
..........\threshold_ksw.m
..........\window_function.fig
..........\window_function.m
..........\datas\ksw_2d_ga_improve.fig
..........\.....\ksw_2d_ga_qiongju_noise.fig
..........\.....\ksw_ga_improve.fig
..........\.....\ksw_ga_improve_noise.fig
..........\.....\ksw_qiongju.fig
..........\.....\ksw_qiongju_noise.fig
..........\.....\二维最佳直方图熵法及传统遗传算法.txt
..........\.....\二维最佳直方图熵法及改进遗传算法.txt
..........\.....\二维最佳直方图熵法及穷举法.txt
..........\.....\最佳直方图熵法及改进遗传算法.txt
..........\.....\最佳直方图熵法及穷举法.txt
..........\AI.m
..........\clone.m
..........\cross111.m
..........\mutation111.m
..........\hebing.m
..........\AI.asv
..........\oushi.m
..........\datas
segment_ga
..........\2d_ksw_qiongju.m
..........\cross.asv
..........\cross.m
..........\cross1.asv
..........\cross1.m
..........\cross_2d.asv
..........\cross_2d.m
..........\cross_2d1.m
..........\ga_main.asv
..........\ga_main.m
..........\hist2.m
..........\ksw.asv
..........\ksw.m
..........\ksw_2d.asv
..........\ksw_2d.m
..........\ksw_2d_ga.asv
..........\ksw_2d_ga.m
..........\ksw_2d_ga_improve.asv
..........\ksw_2d_ga_improve.m
..........\ksw_2d_qiongju.m
..........\ksw_ga.asv
..........\ksw_ga.m
..........\ksw_ga_improve.asv
..........\ksw_ga_improve.m
..........\ksw_qiongju.asv
..........\ksw_qiongju.m
..........\Lenna.bmp
..........\Lenna_noise.bmp
..........\mutation.m
..........\mutation1.m
..........\mutation_2d.asv
..........\mutation_2d.m
..........\mutation_2d1.m
..........\otosu.asv
..........\otosu.m
..........\rice.tif
..........\rice_noise.tif
..........\rice_noise1.tif
..........\select.asv
..........\select.m
..........\select1.asv
..........\select1.m
..........\select_2d.asv
..........\select_2d.m
..........\select_2d1.asv
..........\select_2d1.m
..........\sourseimage.m
..........\threshold_ksw.asv
..........\threshold_ksw.m
..........\window_function.fig
..........\window_function.m
..........\datas\ksw_2d_ga_improve.fig
..........\.....\ksw_2d_ga_qiongju_noise.fig
..........\.....\ksw_ga_improve.fig
..........\.....\ksw_ga_improve_noise.fig
..........\.....\ksw_qiongju.fig
..........\.....\ksw_qiongju_noise.fig
..........\.....\二维最佳直方图熵法及传统遗传算法.txt
..........\.....\二维最佳直方图熵法及改进遗传算法.txt
..........\.....\二维最佳直方图熵法及穷举法.txt
..........\.....\最佳直方图熵法及改进遗传算法.txt
..........\.....\最佳直方图熵法及穷举法.txt
..........\AI.m
..........\clone.m
..........\cross111.m
..........\mutation111.m
..........\hebing.m
..........\AI.asv
..........\oushi.m
..........\datas
segment_ga