Design of Unsupervised Learning for Automatic Segmentation

B. Suresh Kumar

ISBN 10: 6200224668 / ISBN 13: 9786200224668

Published by LAP LAMBERT Academic Publishing Jul 2019, 2019

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -The image segmentation is used to change or simplify the image representation for the purpose of easy understanding or quicker analysis. Image segmentation is a process of segmenting an image into groups of pixels based on some criterions. Image segmentation is the process of partitioning a digital image into multiple segments. The purpose of image segmentation is to partition an image into meaningful regions with respect to particular application. The image segmentation is used for various applications such as medical images, Satellite images, content based image retrieval, machine vision, Recognition Tasks and Video Surveillance. There are so many methods used for segmentations such as compression based methods, thresholding, and clustering. The clustering methods can be divided into two parts namely supervised and unsupervised. Supervised clustering involves predefining the cluster size for segmenting whereas unsupervised segmentation segments by its own cluster values. 136 pp. Englisch.

Seller Inventory # 9786200224668

Bibliographic Details

• Title: Design of Unsupervised Learning for Automatic Segmentation
• Publisher: LAP LAMBERT Academic Publishing Jul 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 6200224668
• ISBN 13: 9786200224668
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

The image segmentation is used to change or simplify the image representation for the purpose of easy understanding or quicker analysis. Image segmentation is a process of segmenting an image into groups of pixels based on some criterions. Image segmentation is the process of partitioning a digital image into multiple segments. The purpose of image segmentation is to partition an image into meaningful regions with respect to particular application. The image segmentation is used for various applications such as medical images, Satellite images, content based image retrieval, machine vision, Recognition Tasks and Video Surveillance. There are so many methods used for segmentations such as compression based methods, thresholding, and clustering. The clustering methods can be divided into two parts namely supervised and unsupervised. Supervised clustering involves predefining the cluster size for segmenting whereas unsupervised segmentation segments by its own cluster values.