Hierarchical neural networks for image interpretation / Sven Behnke.

By: Behnke, Sven Material type: Text

TextSeries: Serienbezeichnung | Lecture notes in computer science ; 2766.Publication details: Berlin ; New York : Springer, ©2003. Description: 1 online resource (xii, 224 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9783540451693; 3540451692Subject(s): Computer vision | Image processing -- Digital techniques | Neural networks (Computer science) | Computer vision | Image processing -- Digital techniques | Neural networks (Computer science)Genre/Form: Electronic books. Additional physical formats: Print version:Behnke, Sven.: Hierarchical neural networks for image interpretationDDC classification: 006.3/7 LOC classification: TA1634 | .B44 2003Other classification: 54.72 | SS 4800 | ST 301 | DAT 760d | DAT 770d | DAT 708d | DAT 717d Online resources: Click here to access online

Contents:

Neurobiological background -- Related work -- Neural abstraction pyramid architecture -- Unsupervised learning -- Supervised learning -- Recognition of meter values -- Binarization of matrix code -- Learning iterative image reconstruction -- Face localization -- Summary and conclusions.

Summary: 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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Includes bibliographical references and index.

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.

English.