Road recognition in high resolution SAR image based on genetic algorithm

Li, Zhulin; Zhang, Xianwei; Zhang, Honglei

doi:10.1109/wcica.2008.4593960

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…A genetic search process is composed of three stages, namely, selection, crossover and mutation. GAs are widely used for pattern recognition applications, such as handwriting recognition [18], road recognition [19], license plate recognition [20], face recognition [21], and blood cell recognition, [22] among others, because of their excellent characteristics.…”

Section: Affine Parameter Estimation Based On Gamentioning

confidence: 99%

Genetic Algorithm-based Affine Parameter Estimation for Shape Recognition

Mao

Wang

et al. 2014

International Journal of Advanced Robotic Systems

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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 affineinvariant 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.

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