1.

Record Nr.

UNISA996465961503316

Autore

Behnke Sven

Titolo

Hierarchical Neural Networks for Image Interpretation [[electronic resource] /] / by Sven Behnke

Pubbl/distr/stampa

Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003

ISBN

3-540-45169-2

Edizione

[1st ed. 2003.]

Descrizione fisica

1 online resource (XIII, 227 p.)

Collana

Lecture Notes in Computer Science, , 0302-9743 ; ; 2766

Disciplina

006.37

Soggetti

Computers

Neurosciences

Algorithms

Artificial intelligence

Optical data processing

Pattern recognition

Computation by Abstract Devices

Algorithm Analysis and Problem Complexity

Artificial Intelligence

Image Processing and Computer Vision

Pattern Recognition

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Bibliographic Level Mode of Issuance: Monograph

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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.

Sommario/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.