Hierarchical Neural Networks for Image Interpretation
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 lim...
| Main Author: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2003, 2003
|
| Edition: | 1st ed. 2003 |
| Series: | Lecture Notes in Computer Science
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of 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