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...
Enregistré dans:
| Hovedforfatter: | |
|---|---|
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serier: | Lecture notes in computer science
2766 |
| Fag: | |
| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Kommentar: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Hierarchical neural networks for image interpretation, Sven Behnke, Berlin, Springer, 2003, 1 vol. (XII-224 p.), Lecture notes in computer science, 3-540-40722-7 • Hierarchical Neural Networks for Image Interpretation, Texte imprimé, 9783662202609 |
Indholdsfortegnelse:
- 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.