Open AccessGenetic Algorithm-based Affine Parameter Estimation for Shape Recognition
Author(s) -
Yuxing Mao,
Yan Wang,
Quanlin Wang,
Wei He
Publication year - 2014
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/58639
Subject(s) - affine transformation , affine shape adaptation , harris affine region detector , pattern recognition (psychology) , affine hull , computer science , algorithm , centroid , fitness function , affine combination , artificial intelligence , transformation (genetics) , affine coordinate system , invariant (physics) , similitude , rotation (mathematics) , genetic algorithm , mathematics , affine space , geometry , machine learning , biochemistry , chemistry , mathematical physics , gene
Shape recognition is a classically difficult problem because of the affine transformation between two shapes. The current study proposes an affine parameter estimation method for shape recognition based on a genetic algorithm (GA). The contributions of this study are focused on the extraction of affine-invariant features, the individual encoding scheme, and the fitness function construction policy for a GA. First, the affine-invariant characteristics of the centroid distance ratios (CDRs) of any two opposite contour points to the barycentre are analysed. Using different intervals along the azimuth angle, the different numbers of CDRs of two candidate shapes are computed as representations of the shapes, respectively. Then, the CDRs are selected based on predesigned affine parameters to construct the fitness function. After that, a GA is used to search for the affine parameters with optimal matching between candidate shapes, which serve as actual descriptions of the affine transformation between the shapes. Finally, the CDRs are resampled based on the estimated parameters to evaluate the similarity of the shapes for classification. The experimental results demonstrate the robust performance of the proposed method in shape recognition with translation, scaling, rotation and distortion