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The feature extracted by the rotation invar -iants of radial Tchebichef moments could make the image analysis more effectively. However, the classical method adopted integral point sampling which has defects of too many sampling points in the centre of unit circle and insufficient samplings on the edge of the circle. It reduces the efficiency of feature extraction. In order to resolve this problem,...
Cosine transform seldom applies to pattern and object recognition due to its poor capabilities in describing image and calculating efficiency. In order to resolve these problems, a Bi-discrete radial cosine transform is proposed. The new cosine transform has two particular properties. First it takes Mukundan's square-to-circular transformation to project the square to the circle grids. Second different...
In order to effectively apply Pseudo-Zernike moments to the image analysis and the pattern recognition, a novel algorithm for accurate computation of Pseudo-Zernike moments is proposed. This new algorithm introduces a triangle-integration method, and can overcome the drawbacks of the existing method. The performance of the algorithm is experimentally examined using grayscale image, and it shows that...
The error analysis and analytic characteristics of pseudo-Zernike moments influence their feature extraction capabilities seriously. However, there are few researches in the accurate computation of pseudo-Zernike moments. For the purpose of effectively applying Pseudo-Zernike moments to the image analysis and the pattern recognition, a novel algorithm for accurate computation of Pseudo-Zernike moments...
Machine learning algorithms are known to degrade in performance when facing with many features that are not necessary in the field of artificial intelligence and pattern recognition. Rough set theory is a new effective tool in dealing with vagueness and uncertainty information. Attribute reduction is one of the most important concepts in rough set theory and application research. Once it gets the...
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