000 03140nam a22006255i 4500
001 978-3-540-45169-3
003 DE-He213
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007 cr nn 008mamaa
008 121227s2003 gw | s |||| 0|eng d
020 _a9783540451693
_9978-3-540-45169-3
024 7 _a10.1007/b11963
_2doi
050 4 _aQA75.5-76.95
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aUYA
_2thema
082 0 4 _a004.0151
_223
100 1 _aBehnke, Sven.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aHierarchical Neural Networks for Image Interpretation
_h[electronic resource] /
_cby Sven Behnke.
250 _a1st ed. 2003.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2003.
300 _aXIII, 227 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v2766
505 0 _aI. Theory -- Neurobiological Background -- Related Work -- Neural Abstraction Pyramid Architecture -- Unsupervised Learning -- Supervised Learning -- II. Applications -- Recognition of Meter Values -- Binarization of Matrix Codes -- Learning Iterative Image Reconstruction -- Face Localization -- Summary and Conclusions.
520 _aHuman performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks.
650 0 _aComputer science.
650 0 _aNeurosciences.
650 0 _aAlgorithms.
650 0 _aArtificial intelligence.
650 0 _aComputer vision.
650 0 _aPattern recognition systems.
650 1 4 _aTheory of Computation.
650 2 4 _aNeuroscience.
650 2 4 _aAlgorithms.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer Vision.
650 2 4 _aAutomated Pattern Recognition.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540407225
776 0 8 _iPrinted edition:
_z9783662202609
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v2766
856 4 0 _uhttps://doi.org/10.1007/b11963
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
912 _aZDB-2-BAE
942 _cSPRINGER
999 _c189259
_d189259