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001			85206
003			ES-MaUEC
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007			cr nn 008mamaa
008			151107s2016 gw | s |||| 0|eng d
020			_a9783319264622
040			_aES-MaUEC
050		4	_aTA347.E96 _bC848 2016 EB
082	0	4	_a006.3
100	1		_aCuevas, Erik. _944351 _0comprobar XX4978079
245	1	0	_aApplications of Evolutionary Computation in Image Processing and Pattern Recognition _cby Erik Cuevas, Daniel Zaldívar, Marco Perez-Cisneros
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XV, 274 p.) _b111 ilustraciones, 55 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aIntelligent Systems Reference Library _x1868-4394 _v100
505	0		_aIntroduction -- Image Segmentation Based on Differential Evolution Optimization.-Motion Estimation Based on Artificial Bee Colony (ABC) -- Ellipse Detection on Images Inspired by the Collective Animal Behavior -- Template Matching by Using the States of Matter Algorithm -- Estimation of Multiple View Relations Considering Evolutionary Approaches -- Circle Detection on Images Based on an Evolutionary Algorithm that Reduces the Number of Function Evaluations -- Otsu and Kapur Segmentation Based on Harmony Search Optimization -- Leukocyte Detection by Using Electromagnetism-Like Optimization -- Automatic Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms.
520			_aThis book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an optimization task. The book has been structured so that each chapter can be read independently from the others. It can serve as reference book for students and researchers with basic knowledge in image processing and EC methods.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988	0	0	_aEBOOK, EBSPRINGER
650		7	_aInteligencia artificial _0comprobar BNE19900997218 _2embne _9413115
650	0	4	_9669495 _aProceso de imágenes _0LocalX
650	0	7	_aIngeniería _vCongresos y asambleas _0LocalX _2embne _9670301
700	1		_aZaldívar, Daniel. _944352 _0comprobar XX4978081
700	1		_aPerez-Cisneros, Marco. _944353 _0comprobar XX4978082
830		0	_aIntelligent Systems Reference Library _x1868-4394 _v100 _9134112
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-26462-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319264622
907			_a.b12944841 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aTA347.E96 C848 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11588834 _z06-04-17
999			_c85206 _d85206 _x1