.png){kind=icon}
返回
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.patrec.2007.09.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved. The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance. The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization. Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time. (c) 2007 Elsevier B.V. All rights reserved.
Keyword:
multilevel thresholding
image segmentation
histogram
recursion
sub-range
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:
3.3
论文数:
7.9K
被引数:
1.6W
机构
引用论文
Promoting Pollinating Insects in Intensive Agricultural Matrices: Field-Scale Experimental Manipulation of Hay-Meadow Mowing Regimes and Its Effects on Bees.png)
PLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏Durable goods and residential demand for energy and water: evidence from a field trial耐用品和住宅对能源和水的需求: 来自现场试验的证据
PREAI
分享收藏分享收藏
查看更多内容