中国科学院机构知识库网格
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Automatic segmentation of solar granulations based on Morphology technique 会议论文  OAI收割
2014 11th World Congress on Intelligent Control and Automation, WCICA 2014, Shenyang, China, 2014-06-29
作者:  
Deng LH(邓林华)
收藏  |  浏览/下载:30/0  |  提交时间:2016/04/06
Automatic segmentation of granules of the solar photosphere using morphological reconstruction and watershed transform 会议论文  OAI收割
6th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Shenyang Inst Engn, Shenyang, PEOPLES R CHINA, 2013-11-01
作者:  
Feng, Song
收藏  |  浏览/下载:26/0  |  提交时间:2016/04/06
集成电路图像有源区分割方法 学位论文  OAI收割
工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2006
作者:  
刘佳璐
收藏  |  浏览/下载:50/0  |  提交时间:2015/09/02
A new segmentation method of CR images based on discrete wavelet transform and mathematics morphology (EI CONFERENCE) 会议论文  OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
作者:  
Li Z.;  Li Z.
收藏  |  浏览/下载:68/0  |  提交时间:2013/03/25
In this paper  we propose a segmentation method of CR(computed radiography) images with being rid of under-segmentation and over-segmentation. An under-segmentation occurs when pixels belonging to different objects are grouped into a single region. Such errors are the most dangerous because they can invalidate the whole segmentation process. The phenomenon always takes place when we segment CR images because of the principle of CR. In order to depressed under-segmentation  we enhance the CR images using DWT (discrete wavelet transform) to get more detail of CR image features. As we enhance the CR image  the over-segmentation maybe occurs. Compared with under-segmentation  the over-segmentation occurs when a single objects is subdivided by segmentation into several region. For the purpose of preventing from the over-segmentation  we present a scheme for enhanced CR images based on watershed algorithm that solves over-segmentation problem. We use marker-based watershed algorithm. Together with gradient image and marker image  watershed segmentation can make sure to partition CR image into meaningful object and avoid further segmentation of homogeneous regions. The result of the proposed algorithm are compared with those of other standard methods. Experiments have shown a better result and indeed solved under-segmentation and over-segmentation problems.