Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Genetic programming and emergent intelligence
Advances in genetic programming
Edge detector evaluation using empirical ROC curves
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Non-destructive Depth-Dependent Crossover for Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Comparison of Edge Detectors: A Methodology and Initial Study
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Receiver operating characteristic curves and optimal Bayesian operating points
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
IEEE Transactions on Image Processing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Comparison of three evolutionary strategies for multiobjective genetic programming
Artificial Intelligence Review
A generic multi-dimensional feature extraction method using multiobjective genetic programming
Evolutionary Computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Feature extraction and classification by genetic programming
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
The estimation of hölderian regularity using genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Genetic programming for edge detection based on accuracy of each training image
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Interest point detection through multiobjective genetic programming
Applied Soft Computing
Evolving estimators of the pointwise Hölder exponent with Genetic Programming
Information Sciences: an International Journal
Genetic programming for edge detection using blocks to extract features
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Two-Tier genetic programming: towards raw pixel-based image classification
Expert Systems with Applications: An International Journal
Automatic construction of invariant features using genetic programming for edge detection
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Figure of merit based fitness functions in genetic programming for edge detection
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Genetic programming for automatic construction of variant features in edge detection
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Automatic construction of gaussian-based edge detectors using genetic programming
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Genetic programming for edge detection using multivariate density
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In this paper we describe a generic methodology to create an "optimal" feature extraction pre-processing stage for pattern classification. Our aim is to map the input data into a new, one-dimensional feature space in which separability is maximized under a simple thresholding classification. We have used multi-objective genetic programming with Pareto strength-based ranking to bias the selection procedure. The methodology is applied to the edge detection problem in image processing; we make quantitative comparison with the pre-processing stages of the well-known Canny edge detector using synthetic and real-world edge data and conclude that the performance of our evolutionary-based method is much superior to the Canny algorithm based on the criterion of minimum Bayes risk.