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[null,"1.","Record Nr.",null,"UNICAMPANIASUN0113605"]
[null,null,"Titolo",null,"Fractal geometry and stochastics 5. / Christoph Bandt, Kenneth Falconer, Martina Zahle editors"]
[null,null,"Pubbl/distr/stampa",null,"X, 340 p., : ill. ; 24 cm"]
[null,null,"Edizione",null,"[[Cham] : Birkhäuser : Springer, 2015]"]
[null,null,"Descrizione fisica",null,"Pubblicazione in formato elettronico"]
[null,null,"Soggetti",null,"60Jxx - Markov processes [MSC 2020]"]
[{"$nulls":4},"28A80 - Fractals [MSC 2020]"]
[{"$nulls":4},"28A78 - Hausdorff and packing measures [MSC 2020]"]
[{"$nulls":4},"37-XX - Dynamical systems and ergodic theory [MSC 2020]"]
[{"$nulls":4},"60-XX - Probability theory and stochastic processes [MSC 2020]"]
[{"$nulls":4},"60Dxx - Geometric probability and stochastic geometry [MSC 2020]"]
[{"$nulls":4},"37Axx - Ergodic theory [MSC 2020]"]
[{"$nulls":4},"28A75 - Length, area, volume, other geometric measure theory [MSC 2020]"]
[{"$nulls":4},"37Cxx - Smooth dynamical systems: general theory [MSC 2020]"]
[{"$nulls":4},"60Fxx - Limit theorems in probability theory [MSC 2020]"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910144025803321"]
[null,null,"Autore",null,"Behnke Sven"]
[null,null,"Titolo",null,"Hierarchical Neural Networks for Image Interpretation / / by Sven Behnke"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003"]
[null,null,"ISBN",null,"3-540-45169-2"]
[null,null,"Edizione",null,"[1st ed. 2003.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XIII, 227 p.)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 0302-9743 ; ; 2766"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Computers"]
[{"$nulls":4},"Neurosciences"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Optical data processing"]
[{"$nulls":4},"Pattern perception"]
[{"$nulls":4},"Computation by Abstract Devices"]
[{"$nulls":4},"Algorithm Analysis and Problem Complexity"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Image Processing and Computer Vision"]
[{"$nulls":4},"Pattern Recognition"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"I. Theory -- Neurobiological Background -- Related Work -- Neural Abstraction Pyramid Architecture -- Unsupervised Learning -- Supervised Learning -- II. Applications -- Recognition of Meter Values -- Binarization of Matrix Codes -- Learning Iterative Image Reconstruction -- Face Localization -- Summary and Conclusions."]
[null,null,null,"Sommario/riassunto","Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the "]
[{"$nulls":4},"proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks."]