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008			211029s2022 sz | s |||| 0|eng d
020			_a9783030812300
024	7		_a10.1007/978-3-030-81230-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1638.4 _b2022 EB
100	1		_aSiddiqui, Fasahat Ullah _eautor _9687395
245	1	0	_aClustering Techniques for Image Segmentation _cby Fasahat Ullah Siddiqui, Abid Yahya
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XX, 108 páginas) _b55 ilustraciones, 16 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9413188 _aProceso digital de imágenes
700	1		_9673542 _aYahya, Abid _eautor
776	0	8	_iPrinted edition: _z9783030812294
776	0	8	_iPrinted edition: _z9783030812317
776	0	8	_iPrinted edition: _z9783030812324
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-81230-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eu _zSI