Artificial Neural Networks for Computer Vision (Research Notes in Neural Computing)

Yi-Tong Zhou, Dirk Gilbert, Glenn Reinhardt

ISBN 10: 0387976833 / ISBN 13: 9780387976839

Verlag: Springer 1991-12-23, 1991

Beschreibung

Bestandsnummer des Verkäufers 6666-IUK-9780387976839

Bibliografische Details

• Titel: Artificial Neural Networks for Computer Vision (Research Notes in Neural Computing)
• Verlag: Springer 1991-12-23
• Erscheinungsjahr: 1991
• Sprache: Englisch
• ISBN-10: 0387976833
• ISBN-13: 9780387976839
• Einband: Paperback
• Zustand: New

Über diesen Titel

Inhaltsangabe

This book describes artificial neural network (ANN) based algorithms for early vision. The book focuses on several important early vision problems such as static and motion stereo, motion estimation and restoration, and emphasizes finding effective solutions to these problems, using the ANN. Many practical real time image data are provided. Although the book is written for researchers and engineers, it provides a fairly complete and readable introduction to both neural networks and computer vision. Readers can also expect to derive a better understanding of the interaction between these two fields.

Reseña del editor

This monograph is an outgrowth of the authors' recent research on the de­ velopment of algorithms for several low-level vision problems using artificial neural networks. Specific problems considered are static and motion stereo, computation of optical flow, and deblurring an image. From a mathematical point of view, these inverse problems are ill-posed according to Hadamard. Researchers in computer vision have taken the "regularization" approach to these problems, where one comes up with an appropriate energy or cost function and finds a minimum. Additional constraints such as smoothness, integrability of surfaces, and preservation of discontinuities are added to the cost function explicitly or implicitly. Depending on the nature of the inver­ sion to be performed and the constraints, the cost function could exhibit several minima. Optimization of such nonconvex functions can be quite involved. Although progress has been made in making techniques such as simulated annealing computationally more reasonable, it is our view that one can often find satisfactory solutions using deterministic optimization algorithms.