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.
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.
Hierarchical Neural Networks for Image Interpretation
227
Hierarchical Neural Networks for Image Interpretation
227Paperback(2003)
Product Details
| ISBN-13: | 9783540407225 |
|---|---|
| Publisher: | Springer Berlin Heidelberg |
| Publication date: | 09/29/2003 |
| Series: | Lecture Notes in Computer Science , #2766 |
| Edition description: | 2003 |
| Pages: | 227 |
| Product dimensions: | 6.10(w) x 9.25(h) x 0.02(d) |