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020 _a9783319738918
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024 7 _a10.1007/978-3-319-73891-8
_2doi
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_2bicssc
072 7 _aTEC009000
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072 7 _aUYQ
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082 0 4 _a006.3
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245 1 0 _aBridging the Semantic Gap in Image and Video Analysis
_h[electronic resource] /
_cedited by Halina Kwaśnicka, Lakhmi C. Jain.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aX, 163 p. 59 illus., 48 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v145
505 0 _aSemantic Gap in Image and Video Analysis: An Introduction -- Low-Level Feature Detectors and Descriptors for Smart Image and Video Analysis: A Comparative Study -- Scale-insensitive MSER Features: A Promising Tool for Meaningful Segmentation of Images -- Active Partitions in Localization of Semantically Important Image Structures -- Model-based 3D Object recognition in RGB-D Images -- Ontology-Based Structured Video Annotation for Content-Based Video Retrieval via Spatiotemporal Reasoning -- Deep Learning – a New Era in Bridging the Semantic Gap.
520 _aThis book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.
650 0 _aComputational intelligence.
_97716
650 0 _aSemiotics.
_956496
650 0 _aArtificial intelligence.
_93407
650 0 _aSignal processing.
_94052
650 0 _aComputer vision.
_956497
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aSemiotics.
_956496
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aComputer Vision.
_956498
700 1 _aKwaśnicka, Halina.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_956499
700 1 _aJain, Lakhmi C.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_956500
710 2 _aSpringerLink (Online service)
_956501
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783319738925
776 0 8 _iPrinted edition:
_z9783030088798
830 0 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v145
_956502
856 4 0 _uhttps://doi.org/10.1007/978-3-319-73891-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
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