000 03002nam a22005655i 4500
001 978-3-540-45169-3
003 DE-He213
005 20160624101958.0
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 _aUYZG
_2bicssc
072 7 _aCOM037000
_2bisacsh
082 0 4 _a004.0151
_223
100 1 _aBehnke, Sven.
_eauthor.
245 1 0 _aHierarchical Neural Networks for Image Interpretation
_h[electronic resource] /
_cby Sven Behnke.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2003.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_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,
_x0302-9743 ;
_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 _aComputer software.
650 0 _aArtificial intelligence.
650 0 _aComputer vision.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aComputation by Abstract Devices.
650 2 4 _aNeurosciences.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aPattern Recognition.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540407225
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v2766
856 4 0 _uhttp://dx.doi.org/10.1007/b11963
942 _2EBK5224
_cEBK
999 _c34518
_d34518