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MohammadHouman Abbasian

Position:              PhD Graduate Student
Starting Date:    Sept. 2008
Office:               ENSC 8850
Email:               haa31[at]sfu[dot]ca

Academic Background:

M.Sc., Artificial Intelligence and Robotics, Computer Engineering Dept.,Iran University of Science and Technology (IUST), Tehran, Iran (2006)

B.Sc., Software Engineering, Computer Engineering Dept., Isfahan University, Isfahan, Iran (2003)



- Pattern Recognition

Farsi Handwritten Segmentation and Recognition, New Fuzzy Cluster Validation Based on Covariance of Partition Matrix, An Evolutionary Approach to Multi Objective Clustering with Automatic Determination of Number of Clusters

- Neural Networks

Research on New Training Algorithms of MLP & RBF Networks, Function Approximation Using Fuzzy Rules and Fuzzy Neural Networks

- Genetic Algorithm

Function Approximation Using Genetic Programming

- Speech Synthesis and Recognition

Implementation of Nonlinear Principle Component Based on Neural Networks And Genetic Programming ,and Kernel Principle Component Application to Speech Recognition for Robust Speech Feature Selection, and New Feature Optimization, for Robust Speech Recognition Based on Genetic Programming Principle Component Analysis.(MS Thesis)

- Fuzzy Methods and Systems

Implementation of Fuzzy Control of Cart Ball, and Assessment of Different Fuzzy Clustering Algorithms on Multi Spectral Data


H. Abbasian, E. Adeli, M. Izadi, and M. Fathi, “Clustering based non parametric model for tracking and cast shadow detection of moving object,” 8th international conference on pattern recognition and image analysis, Russia, 2007.

H.Abbasian, B.Nasersharif,A.Akbari “ Optimized Linear Discriminant Analysis for Extracting Speech Features”, Third International Symposium on Communications, Control and Signal Processing (ISCCSP 2008). St. Julians, Malta.

Research Interests:

Machine Vision, Medical Image Processing, Remote Sensing, Pattern Recognition

Other Interests:


Hobbies & Activities

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The Laboratory for Robotic Vision