检索结果-南通市图书馆

Improved accuracy of superpixel segmentation by region merging method

Frontiers of Optoelectronics 2016年第4期9卷 633-639页

作者： Song ZHU Danhua CAO Yubin WU Shixiong JIANG School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan 430074 China

Superpixel as an important pre-processing technique has been successfully used in many vision applications. In this paper, we proposed a region merging method to improve superpixel segmentation accuracy with low computational cost. We first segmented the image into many accurate small regions, and then progressively agglomerated them until the desired region number was reached. The region merging weight was derived from a novel energy function, which encourages the superpixel with color consistency and similar size. Experimental results on the Berkeley BSDS500 data set showed that our region merging method can significantly improve the accuracy of superpixel segmentation. Moreover, the region merging method only need 50ms to process a 481x321 image on a single Intel i3 CPU at 2.5 GHz.

关键词： image processing image segmentation super-pixels region merging

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Image segmentation method for coal particle size distribution analysis

引用

Particuology 2021年第3期19卷 163-170页

作者： Feiyan Bai Minqiang Fan Hongli Yang Lianping Dong College of Mining Engineering Taiyuan University of TechnologyTaiyuan 030024China

Particle size distribution is extremely important in the coal preparation *** is traditionally analysed by a manual screening method,which is relatively time-consuming and cannot immediately guide *** this paper,an image segmentation method for images of coal particles is *** employs the watershed algorithm,k-nearest neighbour algorithm,and convex shell method to achieve preliminary segmentation,merge small pieces with large pieces,and split adhered particles,*** the automated segmentation using this method with manual segmentation,it is found that the results are *** size distributions obtained by the automated and manual segmentation methods are nearly identical,and the standard deviation is less than 3%,indicating good *** automated image segmentation method provides a new approach for rapidly analysing the size distribution of coal particles with size fractions defined according to consumer requirements.

关键词： Particle size distribution Image segmentation Watershed algorithm using gradient KNN algorithm region merging Convex shell

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

EFFICIENT IMAGE SEGMENTATION FOR SEMANTIC OBJECT GENERATION

引用

Journal of Electronics(China) 2002年第4期19卷 420-425页

作者： Chen Xiaotang Yu Yinglin (Dept. of Comm. & Info. Eng., South China Univ. of Technology, Guangzhou 510640) Dept. of Comm. & Info. Eng. South China Univ. of Technology Guangzhou 510640

This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation. First, the image is filtered using contour-preserving filters. Then it is quasi-flat labeled. The small regions near the contour are classified as uncertain regions and are eliminated by region growing and merging. Further region merging is used to reduce the region number. The simulation results show its efficiency and simplicity. It can preserve the semantic object shape while emphasize on the perceptual complex part of the object. So it conforms to the human visual perception very well.

关键词： Image segmentation Semantic object Contour-preserving noise filtering Quasi-flat regions labeling region merging

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：