Clustering techniques for image segmentation / Fasahat Ullah Siddiqui, Abid Yahya.
Siddiqui, Fasahat Ullah, author.; Yahya, Abid, author.
2022
TA1638.4 .S53 2022
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title Clustering techniques for image segmentation / Fasahat Ullah Siddiqui, Abid Yahya.
Author Siddiqui, Fasahat Ullah, author.
ISBN 9783030812300 (electronic bk.)
3030812308 (electronic bk.)
9783030812294
3030812294
DOI https://doi.org/10.1007/978-3-030-81230-0
Published Cham : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource : illustrations (some color)
Item Number 10.1007/978-3-030-81230-0 doi
Call Number TA1638.4 .S53 2022
Dewey Decimal Classification 621.36/7
Summary This book presents the workings of major clustering techniques along with their advantages and shortcomings. After introducing the topic, the authors illustrate their modified version that avoids those shortcomings. The book then introduces four modified clustering techniques, namely the Optimized K-Means (OKM), Enhanced Moving K-Means-1(EMKM-1), Enhanced Moving K-Means-2(EMKM-2), and Outlier Rejection Fuzzy C-Means (ORFCM). The authors show how the OKM technique can differentiate the empty and zero variance cluster, and the data assignment procedure of the K-mean clustering technique is redesigned. They then show how the EMKM-1 and EMKM-2 techniques reform the data-transferring concept of the Adaptive Moving K-Means (AMKM) to avoid the centroid trapping problem. And that the ORFCM technique uses the adaptable membership function to moderate the outlier effects on the Fuzzy C-meaning clustering technique. This book also covers the working steps and codings of quantitative analysis methods. The results highlight that the modified clustering techniques generate more homogenous regions in an image with better shape and sharp edge preservation. Showcases major clustering techniques, detailing their advantages and shortcomings; Includes several methods for evaluating the performance of segmentation techniques; Presents several applications including medical diagnosis systems, satellite imaging systems, and biometric systems.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed November 16, 2021).
Added Author Yahya, Abid, author.
Available in Other Form Clustering techniques for image segmentation.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction
Introduction to Image Segmentation and Clustering
Hard and Soft Clustering Techniques
New Enhanced Clustering Techniques
Mathematical Model of clustering techniques and evaluation methods
Conclusion.

Browse Subjects

Show more subjects...

Statistics

from
to
Export