Behnke Hierarchical Neural Networks for Image Interpretation

Erscheinungsjahr 2003
ISBN: 978-3-540-45169-3
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2766, 227 Seiten, eBook

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Behnke, Sven

Inhaltsverzeichnis

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.