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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Automatic learning of image filters using Cartesian genetic programming

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Paris, P. C. D. [1] ; Pedrino, E. C. [1] ; Nicoletti, M. C. [1, 2]
Total Authors: 3
[1] Fed Univ S Carlos, Sao Carlos, SP - Brazil
[2] FACCAMP, Campo Limpo Paulista, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: Integrated Computer-Aided Engineering; v. 22, n. 2, p. 135-151, 2015.
Web of Science Citations: 25

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. (AU)

FAPESP's process: 12/13899-9 - Virtual reconfigurable architecture for image processing in real time
Grantee:Emerson Carlos Pedrino
Support type: Regular Research Grants