Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Declarative and Preferential Bias in GP-based Scientific Discovery
Genetic Programming and Evolvable Machines
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Genetic Programming and Evolvable Machines
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synthesis of interest point detectors through genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic programming for finite algebras
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multiobjective design of operators that detect points of interest in images
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Automated design of image operators that detect interest points
Evolutionary Computation
Genetic Programming and Evolvable Machines
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Evolutionary learning of local descriptor operators for object recognition
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Dynamic maximum tree depth: a simple technique for avoiding bloat in tree-based GP
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
An empirical study of functional complexity as an indicator of overfitting in genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
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
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This paper presents a Genetic Programming (GP) approach to synthesize estimators for the pointwise Hölder exponent in 2D signals. It is known that irregularities and singularities are the most salient and informative parts of a signal. Hence, explicitly measuring these variations can be important in various domains of signal processing. The pointwise Hölder exponent provides a characterization of these types of features. However, current methods for estimation cannot be considered to be optimal in any sense. Therefore, the goal of this work is to automatically synthesize operators that provide an estimation for the Hölderian regularity in a 2D signal. This goal is posed as an optimization problem in which we attempt to minimize the error between a prescribed regularity and the estimated regularity given by an image operator. The search for optimal estimators is then carried out using a GP algorithm. Experiments confirm that the GP-operators produce a good estimation of the Hölder exponent in images of multifractional Brownian motions. In fact, the evolved estimators significantly outperform a traditional method by as much as one order of magnitude. These results provide further empirical evidence that GP can solve difficult problems of applied mathematics.