Improving Recognition Effectiveness of Noisy Texture Concepts through Optimization of Their Descriptions

Pachowicz, Peter W.; Bala, Jerzy

doi:10.1016/b978-1-55860-200-7.50127-6

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1994

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Cited by 6 publications

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“…Genetic algorithms have been tested for a variety of problems in computer vision. Several experiments have been led on texture recognition [46] and on rule optimization [44]. Montana has been developing system for classification of sonar signals which automatically optimize parameters of rules.…”

Section: Other Systemsmentioning

confidence: 99%

Machine Learning for Object Recognition and Scene Analysis

Kodratoff

Moscatelli

1994

Int. J. Patt. Recogn. Artif. Intell.

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Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptive target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems. Each approach will be commented on and its strong and weak points will be underlined. In conclusion we will suggest what could be the “ideal” learning system for vision.

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Section: Other Systemsmentioning

confidence: 99%