Hierarchical Neural Networks for Image Interpretation

• Main Author: Behnke, Sven (Author, http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.
• Edition: 1st ed. 2003.
• Series: Lecture Notes in Computer Science, 2766
• Subjects: 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.
• Physical Description: XIII, 227 p. online resource.
• ISBN: 9783540451693
• ISSN: 0302-9743 ;
• DOI: 10.1007/b11963

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.