LEADER 04086nam  22007935  450 
001 9910144025803321
005 20200701161246.0
010   $a3-540-45169-2
024 7 $a10.1007/b11963
035   $a(CKB)1000000000212118
035   $a(SSID)ssj0000323753
035   $a(PQKBManifestationID)11241059
035   $a(PQKBTitleCode)TC0000323753
035   $a(PQKBWorkID)10303298
035   $a(PQKB)10200284
035   $a(DE-He213)978-3-540-45169-3
035   $a(MiAaPQ)EBC3088518
035   $a(PPN)155186086
035   $a(EXLCZ)991000000000212118
100   $a20121227d2003      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aHierarchical Neural Networks for Image Interpretation /$fby Sven Behnke
205   $a1st ed. 2003.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2003.
215   $a1 online resource (XIII, 227 p.) 
225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v2766
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-540-40722-7 
320   $aIncludes bibliographical references and index.
327   $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.
330   $aHuman performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks.
410  0$aLecture Notes in Computer Science,$x0302-9743 ;$v2766
606   $aComputers
606   $aNeurosciences
606   $aAlgorithms
606   $aArtificial intelligence
606   $aOptical data processing
606   $aPattern perception
606   $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013
606   $aNeurosciences$3https://scigraph.springernature.com/ontologies/product-market-codes/B18006
606   $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
615  0$aComputers.
615  0$aNeurosciences.
615  0$aAlgorithms.
615  0$aArtificial intelligence.
615  0$aOptical data processing.
615  0$aPattern perception.
615 14$aComputation by Abstract Devices.
615 24$aNeurosciences.
615 24$aAlgorithm Analysis and Problem Complexity.
615 24$aArtificial Intelligence.
615 24$aImage Processing and Computer Vision.
615 24$aPattern Recognition.
676   $a006.37
700   $aBehnke$b Sven$4aut$4http://id.loc.gov/vocabulary/relators/aut$0564937
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910144025803321
996   $aHierarchical neural networks for image interpretation$9954166
997   $aUNINA