Generic Object Recognition Using a Combination of ICA and Shape Cues

Kalra, Manisha; Das, Sukhendu; Datta, Amitava

doi:10.1109/avss.2006.52

Cited by 2 publications

(2 citation statements)

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“…Researchers have proposed many shape descriptors in the past decades, including the Fourier descriptor [1, 2]. Other image processing approaches, such as principal component analysis (PCA) [3, 4], independent component analysis (ICA) [5, 6], Wavelet Transformation [7, 8], and Neural network [9, 10], have been used to build representations of shapes. Mei and Androutsos [4] proposed two affine-invariant shape descriptors, namely, the ICA-Fourier and the PCA-Fourier descriptors, which gave satisfying performances in shape-based silhouette image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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