Hossein Ebrahimnezhad

Work place: Department of Electrical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

E-mail: ebrahimnezhad@sut.ac.ir


Research Interests: Computer systems and computational processes, Computer Vision, Computer Graphics and Visualization, 2D Computer Graphics, Computer Animation, Image Processing


Hossein Ebrahimnezhad: was born in Iran 1971. He received his B.Sc. and M.Sc. degrees in Electronic and Communication Engineering from Tabriz University and K.N.Toosi University of Technology in 1994 and 1996, respectively. In 2007, he received his Ph.D. degree from Tarbiat Modares University. His research interests include image processing, computer vision, 3D model processing and soft computing. Currently, he is an assistant professor at Sahand University of Technology.

Author Articles
Shape Classification Based on Normalized Distance and Angle Histograms Using PNN

By Atefeh Goshvarpour Hossein Ebrahimnezhad Ateke Goshvarpour

DOI: https://doi.org/10.5815/ijitcs.2013.09.06, Pub. Date: 8 Aug. 2013

This study presents an attempt to develop a reliable computerized algorithm, which could classify images into predetermined classes. For this purpose, the histogram of the normalized distance between each two points of the image (algorithm I) and the histogram of normalized distances between three points and the normalized angle of the image edge points (algorithm II) are analyzed. The probabilistic neural network (PNN) is implemented to do shape classification. Our proposed approach is tested on ten classes of MPEG-7 image database. It has been shown that feature extraction based on the distance histogram (algorithm I and algorithm II) is efficient due to its potential to preserve interclass and intra-class variation. In addition, these algorithms ensur invariance to geometric transformations (e.g. translation, rotation and scaling). The best classification accuracy is achieved by eight classes with the total accuracy of 90% and 92.5% for algorithm I and algorithm II, respectively. The reported experiment reveal that the proposed classification algorithm could be useful in the study of MPEG-7 shapes.

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Classification of Epileptic EEG Signals using Time-Delay Neural Networks and Probabilistic Neural Networks

By Ateke Goshvarpour Hossein Ebrahimnezhad Atefeh Goshvarpour

DOI: https://doi.org/10.5815/ijieeb.2013.01.07, Pub. Date: 8 May 2013

The aim of this paper is to investigate the performance of time delay neural networks (TDNNs) and the probabilistic neural networks (PNNs) trained with nonlinear features (Lyapunov exponents and Entropy) on electroencephalogram signals (EEG) in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy) are analyzed. To evaluate the performance of the classifiers, mean square error (MSE) and elapsed time of each classifier are examined. The results show that TDNN with 12 neurons in hidden layer result in a lower MSE with the training time of about 19.69 second. According to the results, when the sigma values are lower than 0.56, the best performance in the proposed probabilistic neural network structure is achieved. The results of present study show that applying the nonlinear features to train these networks can serve as useful tool in classifying of the EEG signals.

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