國立虎尾科技大學 |

Clustering Techniques for Image Segmentation

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Clustering Techniques for Image Segmentation/ by Fasahat Ullah Siddiqui, Abid Yahya.
作者:	Siddiqui, Fasahat Ullah.
其他作者:	Yahya, Abid.
面頁冊數:	XX, 108 p. 55 illus., 16 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computer Vision. -
電子資源:	https://doi.org/10.1007/978-3-030-81230-0
ISBN:	9783030812300

Clustering Techniques for Image Segmentation
Siddiqui, Fasahat Ullah.

Clustering Techniques for Image Segmentation[electronic resource] /by Fasahat Ullah Siddiqui, Abid Yahya. - 1st ed. 2022. - XX, 108 p. 55 illus., 16 illus. in color.online resource.

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.

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.

ISBN: 9783030812300

Standard No.: 10.1007/978-3-030-81230-0doiSubjects--Topical Terms:

1127422
Computer Vision.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382

Clustering Techniques for Image Segmentation
LDR:03066nam a22004215i 4500 001 1090367
003 DE-He213
005 20220119185939.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030812300 $9 978-3-030-81230-0
024 7 $a 10.1007/978-3-030-81230-0 $2 doi
035 $a 978-3-030-81230-0
050 4 $a TK5102.9
072 7 $a TJF $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TJF $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Siddiqui, Fasahat Ullah. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1397768
245 1 0 $a Clustering Techniques for Image Segmentation $h [electronic resource] / $c by Fasahat Ullah Siddiqui, Abid Yahya.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XX, 108 p. 55 illus., 16 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
520 $a 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.
650 2 4 $a Computer Vision. $3 1127422
650 2 4 $a Computational Intelligence. $3 768837
650 1 4 $a Signal, Speech and Image Processing . $3 1366353
650 0 $a Computer vision. $3 561800
650 0 $a Computational intelligence. $3 568984
650 0 $a Signal processing. $3 561459
700 1 $a Yahya, Abid. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1247173
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030812294
776 0 8 $i Printed edition: $z 9783030812317
776 0 8 $i Printed edition: $z 9783030812324
856 4 0 $u https://doi.org/10.1007/978-3-030-81230-0
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)