Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Morphological algorithm design for binary images using genetic programming
Genetic Programming and Evolvable Machines
Genetic programming for image analysis
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Learning features for object recognition
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Assessment of the influence of adaptive components in trainable surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
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The rapid development in image processing technology allows to tackle applications of increasing complexity. For efficient design of application specific systems, design automation techniques are required. This paper reports on activities for automated texture classification system design employing non-linear oriented kernels (NLOK) configured by evolutionary optimization techniques and swarm optimization (PSO). First and second order statistical features of the dominating kernels in automatically adapted regions of interests serve as features for the texture classification. Our approach was tested with benchmark and application data from leather inspection and was found to be viable and competitive in both cases. The optimized feature set was tested versus features computed from gray value co-occurrence matrices (COOC) with non-optimized parameters. The classification rates for NLOK were significantly higher than for COOC ( 75% vs. 90% vs.