Object Shape Recognition Using Mexican Hat Wavelet Descriptors

Nabout, A.; Tibken, Bernd

doi:10.1109/icca.2007.4376573

Cited by 9 publications

(4 citation statements)

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“…Equation (7) shows that the CDR derived from two opposite contour points to the barycentre will be invariant to affine transformation, and thus, it can be adopted as a feature of shape representation.…”

Section: Definition Of Cdrs and Their Affine Invariant Propertymentioning

confidence: 99%

“…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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Section: Definition Of Cdrs and Their Affine Invariant Propertymentioning

confidence: 99%

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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“…In this paper, we present the derivation of wavelet descriptors using a periodical angle function on the basis of the Mexican Hat and Haar wavelet. The new method is based on the publications listed in [21][22][23][24] and is described in this paper much more coherently and, specifically, provides further details related to the following important points.…”

Section: Introductionmentioning

confidence: 99%

Object Shape Recognition Using Wavelet Descriptors

Nabout

2013

Journal of Engineering

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The wavelet transform is a well-known signal analysis method in several engineering disciplines. In image processing and pattern recognition, the wavelet transform is used in many applications for image coding as well as feature extraction purposes. It can be used to describe a given object shape by wavelet descriptors (WD). Thus, it is used to recognize objects according to their contour shape by deriving a number of WD and comparing them with the WD of stored contour patterns. For our method, we use a periodical angle function derived from an extracted object contour. In order to apply the WD, the Mexican Hat can be used as the mother wavelet. In this paper, the method of object shape recognition using wavelet descriptors is described coherently and includes details relating to the method of applying the periodical angle function and the derivation of the formulas for the Haar as well as Mexican Hat wavelet descriptors. To evaluate the results of object recognition when using wavelet descriptors taking into account the dependence on the starting point, the paper describes a sufficient method for the comparison of wavelet descriptors using the minimum distance matrix.

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