Some of the fundamental constraints of automated machine vision have been the inability to automatically adapt parameter settings or utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to computer vision systems. Using this state-of-the-art recognition technology, the outcome is different adaptive recognition systems that can measure their own performance, learn from their experience and outperform conventional static designs. Written as ...
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Some of the fundamental constraints of automated machine vision have been the inability to automatically adapt parameter settings or utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to computer vision systems. Using this state-of-the-art recognition technology, the outcome is different adaptive recognition systems that can measure their own performance, learn from their experience and outperform conventional static designs. Written as a companion volume to Early Visual Learning (edited by S. Nayar and T. Poggio), this book is intended for researchers and students in machine vision and machine learning.
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Add this copy of Symbolic Visual Learning to cart. $7.99, very good condition, Sold by Wonder Book - Member ABAA/ILAB rated 5.0 out of 5 stars, ships from Frederick, MD, UNITED STATES, published 1997 by Oxford University Press.
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Very Good. Very Good condition. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp. Bundled media such as CDs, DVDs, floppy disks or access codes may not be included.
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Near Fine. No Jacket. 1997 edition. Previous owner's signature to front flyleaf. Near fine book with no dust jacket. All pages clean, bright, unmarked. Strong, tight spine. 355 pp. Octavo.
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