Abstract
In this paper, we propose a new classification system for image databases, particularly valid for noisy and geometrically distorted images. This system consists of three steps. In the first step, we apply a new image denoising technique based on the resolution of the Perona and Malik model using the finite element method (FEM). In the second step, we use the orthogonal invariant moments, applied to the obtained denoised images, to extract the feature vectors of images. In this step, we use a new set of orthogonal polynomials derived from the orthogonal Legendre polynomials, we call them orthogonal adapted-Legendre polynomials. These polynomials are used to define a series of orthogonal moments, which are invariant to translation, rotation, and scale. In the third steps, we use the radial basis function neural network (RBF), where the calculated feature vectors are the inputs of the input layer. To show the effectiveness of the proposed approach, we perform experimental tests and a comparative study with other well-known classification systems. The results obtained show the superiority and efficiency of our system.
Published Version
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