000			03500nam a22005415i 4500
999			_c200457652 _d75864
003			TR-AnTOB
005			20231114170803.0
007			cr nn 008mamaa
008			211029s2022 sz | s |||| 0|eng d
020			_a9783030812300
024	7		_a10.1007/978-3-030-81230-0 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
041			_aeng
050		4	_aTA1638.4
072		7	_aTJF _2bicssc
072		7	_aUYS _2bicssc
072		7	_aTEC008000 _2bisacsh
072		7	_aTJF _2thema
072		7	_aUYS _2thema
090			_aTA1638.4EBK
100	1		_aSiddiqui, Fasahat Ullah. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aClustering Techniques for Image Segmentation _h[electronic resource] / _cby Fasahat Ullah Siddiqui, Abid Yahya.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- 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			_aThis 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		0	_aSignal processing.
650		0	_aComputational intelligence.
650		0	_aComputer vision.
650	1	4	_aSignal, Speech and Image Processing .
650	2	4	_aComputational Intelligence.
650	2	4	_aComputer Vision.
653		0	_aImage segmentation
653		0	_aCluster analysis
700	1		_aYahya, Abid. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-81230-0 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK