.png){kind=icon}
返回
A multiscale gradient algorithm for image segmentation using watersheds
.png){kind=icon}
PRE
AI
DOI：10.1016/S0031-3203(97)00015-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Watershed transformation is a powerful tool for image segmentation. However, the effectiveness of the image segmentation methods based on watershed transformation is limited by the quality of the gradient image used in the methods. In this paper we present a multiscale algorithm for computing gradient images, with effective handling of both step and blurred edges. We also present an algorithm for eliminating irrelevant minima in the resulting gradient images. Experimental results indicate that watershed transformation with the algorithms proposed in this paper produces meaningful segmentations, even without a region merging step. The proposed algorithms can efficiently improve segmentation accuracy and significantly reduce the computational cost of watershed-based image segmentation methods. (C) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.
Keyword:
gradient operator
image segmentation
watersheds
mathematical morphology
edge detection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
分享收藏
分享收藏
查看更多内容