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Hierarchical Neural Networks for Image Interpretation / by Sven Behnke.
LIBRA Q341 .P7 2004
Available from offsite location
- Format:
- Book
- Author/Creator:
- Behnke, Sven, author.
- Series:
- Computer Science (Springer-11645)
- Lecture notes in computer science 0302-9743 ; 2766.
- Lecture Notes in Computer Science, 0302-9743 ; 2766
- Language:
- English
- Subjects (All):
- Computers.
- Neurosciences.
- Algorithms.
- Artificial intelligence.
- Optical data processing.
- Pattern perception.
- Computation by Abstract Devices.
- Algorithm Analysis and Problem Complexity.
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Pattern Recognition.
- Local Subjects:
- Computation by Abstract Devices.
- Neurosciences.
- Algorithm Analysis and Problem Complexity.
- Artificial Intelligence.
- Image Processing and Computer Vision.
- Pattern Recognition.
- Physical Description:
- 1 online resource (XIII, 227 pages).
- Edition:
- First edition 2003.
- Contained In:
- Springer eBooks
- Place of Publication:
- Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.
- System Details:
- text file PDF
- 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.
- Contents:
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-540-45169-3
- 9783540451693
- Access Restriction:
- Restricted for use by site license.
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