Automatic learning of image filters using Cartesian genetic programming

Papaggelos Paris

doi:10.3233/ICA-150482

Automatic learning of image filters using Cartesian genetic programming

2015-02-01

44

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AI

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Papaggelos Paris

E

Emerson Carlos Pedrino *

DOI：10.3233/ICA-150482

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This paper proposes a computational modeling for image filtering processes based on the Cartesian Genetic Programming (CGP) methodology, suitable for hardware devices. A computational system named ALIF-CGP (Automatic Learning of Image Filters Using Cartesian Genetic Programming) was designed as a simulator for automatically constructing a sequence of operators, mainly morphological and logical, which can filter a particular shape of image. ALIF-CGP is a convenient option for executing the non-trivial task, usually manually done by human experts, of selecting the sequence of nonlinear operators to be used in morphological filters. ALIF-CGP has already a built-in pool of morphological and logical operators, which can be used by default. The user, however, has the flexibility of choosing only those operators which are of interest or then, conveniently introduce new ones. The system expects as input a pair of images (input-target). The flexibility given by the CGP-based computational modeling used by ALIF-CGP as well as its efficiency and satisfactory results, obtained in various image processing case studies, recommend its use when developing a hardware implementation for the purposes of image filtering. A few case studies using ALIF-CGP are presented and comparatively analyzed in relation to previous results available in the literature.

Keyword:

Cartesian genetic programming (CGP)

CGP-based computational model

mathematical morphology

morphological image filters

automatic construction of image filters

Automatic learning of image filters using Cartesian genetic programming

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