Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

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Titolo: Hierarchical Neural Networks for Image Interpretation: 2766 (Lecture Notes in Computer Science)
Editore: Springer 13 J
Data di pubblicazione: 2003
Lingua: Inglese
ISBN 10: 3540407227
ISBN 13: 9783540407225
Rilegatura: Paperback
Condizione: Very Good

Descrizione articolo

Riassunto

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.

Recensione

From the reviews:

"This booklet is the reprint of a thesis. It addresses image interpretation using a neural network architecture mimicking the human visual system. ... The exposition is divided in two parts, namely theory and applications. ... In short this thesis is very interesting, well written and easy to read." (Jean Th. Laprest�, Zentralblatt MATH, Vol. 1041 (16), 2004)

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