000 | 03140nam a22006255i 4500 | ||
---|---|---|---|
001 | 978-3-540-45169-3 | ||
003 | DE-He213 | ||
005 | 20240423132554.0 | ||
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 |
_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 |