Mehdi Chehel Amirani

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Digital information is packed into files when it is going to be stored on storage media. Each computer file is associated with a type. Type detection of computer data is a building block in different applications of computer forensics and security. Traditional methods were based on file extensions and metadata. The content-based method is a newer approach(More)
Automated and accurate classification of brain MRI is such important that leads us to present a new robust classification technique for analyzing magnetic response images. The proposed method consists of three stages, namely, feature extraction, dimensionality reduction, and classification. We use gray level co-occurrence matrix (GLCM) to extract features(More)
In this paper, we present a new feature extraction method for Electroencephalogram (EEG) signals classification, based on GARCH modeling of wavelet coefficients. First, the EEG signals are decomposed into the frequency sub-bands using discrete wavelet transform (DWT). GARCH model can capture the important statistical properties of wavelet coefficients. We(More)
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Handwritten document recognition is an area of pattern recognition that has been showing impressive performance in the machine printed text. Handwritten document recognition is an intricate task to various writing styles of individual person. The system first identifies the contour in a handwritten document for segmentation and features are extracted from(More)
Utilization of cyclostationarity is a fresh paradigm in texture classification. This paper employs the Strip Spectral Correlation Analyzer (SSCA) as the new and superior method of such a category. The SSCA has been much more computational efficient than the other spectral correlation estimators, such as the FFT-Accumulated Method (FAM) or Direct Frequency(More)
In this paper, a new algorithm which is based on the continues wavelet transformation and local binary patterns (LBP) for content based texture image classification is proposed. We improve the Local Binary Pattern approach with Wavelet Transformation to propose the texture classification. We used 12 classes of Brodatz textures data base for proposed method.(More)