Efficient image segmentation and implementation of K-means clustering

被引：9
|
作者：
Deeparani, K. [1 ]
Sudhakar, P. [2 ]
机构：
[1] Bharathiar Univ, Dept Comp Sci, Coimbatore 641046, TN, India
[2] Annamalai Univ, Dept Comp Sci, Chidambaram 608002, India
来源：
MATERIALS TODAY-PROCEEDINGS | 2021年 / 45卷
关键词：
Image processing; Image segmentation; K-means clustering; Cluster centers; Label function; Reshaping;
D O I：
10.1016/j.matpr.2021.01.154
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The image segmentation scheme is proposed in this research article. It offers the determined the certain images and grouping of images from colleague frame. In this research, the K-Means algorithm is utilized for image segmentation. The images have transformed into Gray scale images then it facilitates the extraction of clusters. The K-Means technique is based on groupings of similar pixels and allocation of the median. Repeating the same process several times provides better object identifications. This algorithm will provide you good quality output images. Object discrimination relies on the correlation of available pixels in the image. After being segmented, the image was further processed for improved visibility. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Materials, Manufacturing, and Machining for Industry 4.0.
引用
收藏
页码：8076 / 8079
页数：4
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