Hierarchical neural networks for image interpretation

Hierarchical neural networks for image interpretation

Zugl.: Berlin, Freie Univ., Diss., 2002

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1. Verfasser: Behnke, Sven (VerfasserIn)
Format: UnknownFormat
Sprache:eng
Veröffentlicht: Berlin, Heidelberg u.a. Springer 2003
Schriftenreihe:Lecture notes in computer science 2766
Schlagworte:
Computer vision > Image processing > Digital techniques > Neural networks (Computer science) > Hochschulschrift > Bildverstehen > Bilderkennung > Maschinelles Lernen > Neuronales Netz > Visuelles System > Hierarchisches System
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Zusammenfassung:Zugl.: Berlin, Freie Univ., Diss., 2002
References S. [209] - 220
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 booksets 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
Beschreibung:XII, 224 S.
zahlr. Ill., graph. Darst
ISBN:3540407227
3-540-40722-7