Segmentation and Classification of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Computer
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
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A Robust Competitive Clustering Algorithm With Applications in Computer Vision
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Edge detection in range images based on scan line approximation
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Reliable Location and Regression Estimates with Application to Range Image Segmentation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
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Robust Estimation for Range Image Segmentation and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biases in the Crossover Landscape
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Progress in Automated Evaluation of Curved Surface Range Image Segmentation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Optimal Range Segmentation Parameters through Genetic Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Comparing Curved-Surface Range Image Segmenters
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
An evolutionary approach for ontology driven image interpretation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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Segmentation of ultrasound breast images: optimization of algorithm parameters
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Medical image processing by using soft computing methods and information fusion
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
Color image segmentation using parallel OptiMUSIG activation function
Applied Soft Computing
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Several range image segmentation algorithms have been proposed, each one to be tuned by a number of parameters in order to provide accurate results on a given class of images. Segmentation parameters are generally affected by the type of surfaces (e.g., planar versus curved) and the nature of the acquisition system (e.g., laser range finders or structured light scanners). It is impossible to answer the question, which is the best set of parameters given a range image within a class and a range segmentation algorithm? Systems proposing such a parameter optimization are often based either on careful selection or on solution space-partitioning methods. Their main drawback is that they have to limit their search to a subset of the solution space to provide an answer in acceptable time. In order to provide a different automated method to search a larger solution space, and possibly to answer more effectively the above question, we propose a tuning system based on genetic algorithms. A complete set of tests was performed over a range of different images and with different segmentation algorithms. Our system provided a particularly high degree of effectiveness in terms of segmentation quality and search time.