TY  - BOOK
AU  - Behnke,Sven
ED  - SpringerLink (Online service)
TI  - Hierarchical Neural Networks for Image Interpretation
T2  - Lecture Notes in Computer Science,
SN  - 9783540451693
AV  - QA75.5-76.95 
U1  - 004.0151 23
PY  - 2003///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Neurosciences
KW  - Computer software
KW  - Artificial intelligence
KW  - Computer vision
KW  - Optical pattern recognition
KW  - Computer Science
KW  - Computation by Abstract Devices
KW  - Algorithm Analysis and Problem Complexity
KW  - Artificial Intelligence (incl. Robotics)
KW  - Image Processing and Computer Vision
KW  - Pattern Recognition
N1  - 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
N2  - 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
UR  - http://dx.doi.org/10.1007/b11963
ER  -