| 基于2维灰度熵阈值选取快速迭代的图像分割 |
| |
| 引用本文： | 吴诗婳,吴一全,周建江,孟天亮,戴一冕.基于2维灰度熵阈值选取快速迭代的图像分割[J].中国图象图形学报,2015,20(8):1042-1050. |
| |
| 作者姓名： | 吴诗婳 吴一全 周建江 孟天亮 戴一冕 |
| |
| 作者单位： | 南京航空航天大学电子信息工程学院, 南京 210016;南京航空航天大学电子信息工程学院, 南京 210016;南京水利科学研究院港口航道泥沙工程交通行业重点实验室, 南京 210024;黄河水利委员会黄河水利科学研究院水利部黄河泥沙重点实验室, 郑州 450003;长江水利委员会长江科学院武汉市智慧流域工程技术研究中心, 武汉 430010;哈尔滨工业大学城市水资源与水环境国家重点实验室, 哈尔滨 150090;安徽理工大学煤矿安全高效开采省部共建教育部重点实验室, 淮南 232001;南京航空航天大学电子信息工程学院, 南京 210016;南京航空航天大学电子信息工程学院, 南京 210016;南京航空航天大学电子信息工程学院, 南京 210016 |
| |
| 基金项目： | 国家自然科学基金项目(60872065);港口航道泥沙工程交通行业重点实验室开放基金;长江科学院开放基金(CKWV2013225/KY);水利部黄河泥沙重点实验室开放基金(2014006);城市水资源与水环境国家重点实验室开放基金(LYPK201304);煤矿安全高效开采省部共建教育部重点实验室(JYBSYS2014102);江苏高校优势学科建设工程资助 |
| |
| 摘 要： | 目的 为了使图像阈值分割的精度和速度进一步提高,提出了一种基于2维灰度熵阈值选取快速迭代的图像分割方法。方法 首先,提出了1维灰度熵阈值选取的快速迭代算法;然后,考虑图像目标和背景的类内灰度均匀性,给出了基于灰度—邻域平均灰度级直方图的灰度熵阈值选取准则;最后,提出了2维灰度熵阈值选取的快速迭代算法,并采用递推方式计算准则函数中的中间变量,避免其重复运算,加快了运算速度,大大减少了运算量。结果 大量实验结果表明,与近年来提出的3种阈值分割法相比,所提出的方法分割性能更优,分割后的图像中目标区域完整,边缘清晰,细节丰富且运行时间短,仅为基于混沌小生境粒子群优化的二维斜分倒数熵分割法运行时间的3%左右。结论 本文方法对不同类型灰度级图像的分割效果及运行速度均有明显优势,是实际系统中可选择的一种快速有效的图像分割方法。
|
| 关 键 词： | 图像分割 阈值选取 灰度熵 快速迭代 |
| 收稿时间： | 2015/1/27 0:00:00 |
| 修稿时间： | 4/7/2015 12:00:00 AM |
Image segmentation based on the fast iteration for two-dimensional gray entropy threshold selection |
| |
| Wu Shihu,Wu Yiquan,Zhou Jianjiang,Meng Tianliang and Dai Yimian.Image segmentation based on the fast iteration for two-dimensional gray entropy threshold selection[J].Journal of Image and Graphics,2015,20(8):1042-1050. |
| |
| Authors: | Wu Shihu Wu Yiquan Zhou Jianjiang Meng Tianliang and Dai Yimian |
| |
| Affiliation: | College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport, Nanjing Hydraulic Research Institute, Nanjing 210024, China;Key Laboratory of the Yellow River Sediment of Ministry of Water Resource, Yellow River Institute of Hydraulic Research, Yellow River Water Resources Commission, Zhengzhou 450003, China;Engineering Technology Research Center of Wuhan Intelligent basin, Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China;State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China;Key Laboratory of Safety and High-efficiency Coal Mining, Ministry of Education, Anhui University of Science and Technology, Huainan 232001, China;College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
| |
| Abstract: | Objective To further improve the accuracy and speed of image threshold segmentation, an image segmentation method is proposed based on fast iteration for two-dimensional gray entropy threshold selection. Method First, a fast iterative algorithm for threshold selection that uses one-dimensional gray entropy is proposed. Gray level uniformity within the object cluster and background cluster is then considered, and two-dimensional gray entropy criterion for threshold selection based on gray level-neighborhood average gray level histogram is presented. Finally, a fast iterative algorithm for threshold selection that uses two-dimensional gray entropy is proposed. In addition, recursive algorithms are adopted to calculate the intermediate variables involved in criterion function, thereby avoiding their repetitive computation. Thus, calculating speed is accelerated and calculation amount is greatly reduced. Result A large number of experimental results show that, compared with three threshold segmentation methods, which have been recently presented, the proposed method has superior image segmentation performance. In the segmented image, object region is complete, edges are clear, and details are rich. Moreover, running time is short and is only approximately 3% of the running time of reciprocal entropy thresholding method with two-dimensional histogram oblique division based on niche chaotic mutation particle swarm optimization. Conclusion The proposed method has obvious advantages in segmentation results and algorithmic running speed for various gray level images. It is a fast and effective segmentation method that can be used in practical systems. |
| |
| Keywords: | image segmentation threshold selection gray entropy fast iteration |
|
| 点击此处可从《中国图象图形学报》浏览原始摘要信息 |
|
点击此处可从《中国图象图形学报》下载免费的PDF全文 |
|
