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In this paper, a recently developed fiber tracking algorithm to be used with diffusion tensor (DT) fields acquired via magnetic resonance imaging (MRI) is improved and applied to real brain DT-MR images. The method performs satisfactorily in regions where branching and crossing fibers exist and offers the capability of reporting a probability value for the computed tracts. This certainty figure takes into account both the anisotropy and the information provided by all the eigenvectors and eigenvalues of the diffusion matrix at each voxel. In previous papers of the authors, a simpler algorithm was applied only to elementary synthetic DT-MR images. As now presented, this algorithm is now adequately used with more intricate synthetic images and is applied to real white matter DT-MR images with successful results. A novel neural network is used to estimate the main parameters of the algorithm. Numerical experiments show a performance gain over previous approaches, specially with respect to convergence and computational load. The tracking of white matter fibers in the human brain will improve the diagnosis and treatment of many neuronal diseases. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to Arabic letter recognition is proposed. The system is based on the classified vector quantization (CVQ) technique employing the minimum distance classifier. To prove the robustness of the CVQ system, its performance is compared to that of a standard artificial neural network (ANN)-based solution. In the CVQ system, each input letter is mapped to its class using the minimum Euclidean distance. Simulation results are provided and show that the CVQ system always produces a lower Mean Squared Error (MSE) and higher success rates than the current ANN solutions. [ABSTRACT FROM AUTHOR]

Face recognition is a biometric identification method which among the other methods such as, finger print identification, speech recognition, signature and hand written recognition has assigned a special place to itself. In principle, the biometric identification methods include a wide range of sciences such as machine vision, image processing, pattern recognition neural networks and has various applications in film processing, control access networks and etc. There are several methods for recognition and appearance based methods is one of them. One of the most important algorithms in appearance based methods is linear discriminant analysis (LDA) method. One of the drawbacks for LDA in face recognition is the small sample size (SSS) problem so it is suggested to first reduce the dimension of the space using methods among which, principal component analysis (PCA) is the most popular one. In this paper we show that there exist stronger methods such as discrete cosine transform (DCT). [ABSTRACT FROM AUTHOR]