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Hierarchical Neural Networks for Image Interpretation

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 lim...

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Bibliographic Details
Main Author: Behnke, Sven
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2003, 2003
Edition:1st ed. 2003
Series:Lecture Notes in Computer Science
Subjects:
Neuroscience
Computer Science
Neurosciences
Computer Vision
Artificial Intelligence
Algorithms
Theory Of Computation
Automated Pattern Recognition
Pattern Recognition Systems
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
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245 0 0 |a Hierarchical Neural Networks for Image Interpretation  |h Elektronische Ressource  |c by Sven Behnke 
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505 0 |a 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 
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653 |a Artificial Intelligence 
653 |a Algorithms 
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653 |a Artificial intelligence 
653 |a Theory of Computation 
653 |a Automated Pattern Recognition 
653 |a Pattern recognition systems 
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490 0 |a Lecture Notes in Computer Science 
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520 |a 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 proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks 

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