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020 _a9789811006319
040 _aES-MaUEC
050 4 _aTA1634
_b.C446 2016 EB
082 0 4 _a621.382
100 1 _aChen, Chen
_0Local
_9100841
245 1 0 _aBig Visual Data Analysis :
_bScene Classification and Geometric Labeling
_cby Chen Chen, Yuzhuo Ren, C-C Jay Kuo
250 _a1st ed.
260 _aSingapore
_bSpringer
_c2016
300 _a1 recurso en línea (X, 122 p.) 94 il., 12 il. col.
336 _aTexto (visual)
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aSpringerBriefs in Electrical and Computer Engineering
_x2191-8112
520 _aThis book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.
942 _2lcc
_cLE
988 _aEBOOK, EBSPRINGER
650 7 _aVisión por ordenador
_0comprobar BNE20020695437
_2embne
_9159793
700 1 _aRen, Yuzhuo
_9100842
_0Local
700 1 _aKuo, C.-C. Jay
_9100843
_0Local
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-981-10-0631-9
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901 _ai9789811006319
907 _a.b12960962
_b10-10-17
_c21-11-16
998 _am
_a_alco
_a_vill
_b03-05-17
_cm
_dz
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_gsi
_h0
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