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    A Hybrid Method for Improving the Color Constancy in Images

    , M.Sc. Thesis Sharif University of Technology Abedini, Zeinab (Author) ; Jamzad, Mansour (Supervisor)
    Abstract
    The ability of measuring colors of objects, independent of light source illumination, is called color constancy which is an important field in machine vision and image processing. In this thesis, we propose five new combinantional ways in color constansy fields. The first two proposed methods use neural networks to combining basic methods. The third and forth proposed methods use fuzzy measures and integrals for combining color constancy methods. And finally fifth method combines methods with indoor outdoor classification this method has the best result (3.01 median angular error) in proposed methods and past methods in color constancy fields. It is shown in this article that the proposed... 

    Feature Extraction for Protein Sequences Based on NMR Spectra and Its Application in the Protein Interaction Prediction

    , M.Sc. Thesis Sharif University of Technology Teimoori, Bahareh (Author) ; Hajsadeghy, Khosro (Supervisor) ; Kavousi, Kaveh (Supervisor)
    Abstract
    Nuclear magnetic resonance is a spectroscopic method which is used to investigate characteristics of molecules with hydrogen and carbon chains. In this thesis we used, NMR spectrum extracted from 19 types of amino acids for investigating on feature generation for protein sequences. We processed NMR spectra based on Hydrogen and Carbon atoms in structure of the amino acids and after preprocessing we extracted features for each amino acid from the spectra. After that, we tried to cluster the amino acids with Fuzzy Clustering Method (FCM) then we generated feature vectors by extracting special descriptor for amino acids in sequence of proteins. In addition to NMR, we used the features of... 

    Combination of multiple classifiers with fuzzy integral method for classifying the EEG signals in brain-computer interface

    , Article ICBPE 2006 - 2006 International Conference on Biomedical and Pharmaceutical Engineering, Singapore, 11 December 2006 through 14 December 2006 ; 2006 , Pages 157-161 ; 8190426249 (ISBN); 9788190426244 (ISBN) Shoaie, Z ; Esmaeeli, M ; Shouraki, S. B ; Sharif University of Technology
    2006
    Abstract
    In this paper we study the effectiveness of using multiple classifier combination for EEG signal classification aiming to obtain more accurate results than it possible from each of the constituent classifiers. The developed system employs two linear classifiers (SVM,LDA) fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While at the measurement level, two types of combination methods have been investigated: one used fixed combination rules that don't require prior training and a trainable combination method. For the second type, the fuzzy integral method was used. The...