Artificial Neural Networks for Computer Vision (1992) (Research Notes in Neural Computing #5)
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- Synopsis
- This monograph is an outgrowth of the authors' recent research on the deĀ 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 inverĀ 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.
- Copyright:
- 1992
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9781461228349
- Related ISBNs:
- 9780387976839
- Publisher:
- Springer New York
- Date of Addition:
- 02/23/21
- Copyrighted By:
- N/A
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Art and Architecture, Computers and Internet, Technology, Communication
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
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