Symbolic Visual Learning

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. <em>Symbolic Visual Learning</em> 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 <em>Early Visual Learning</em> (edited by S. Nayar and T. Poggio) this book is intended for researchers and students in machine vision and machine learning.