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M. Kundu

  1. Classification of Fused Images using Radial Basis Function Neural Network for Human Face Recognition.

    Tue, 07/06/2010 - 15:03 — SomNambul
    Authors: Debotosh Bhattacharjee, M. Kundu, D. K. Basu, M. Nasipuri, M.K. Bhowmik
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    Here an efficient fusion technique for automatic face recognition has been
    presented. Fusion of visual and thermal images has been done to take the
    advantages of thermal images as well as visual images. By employing fusion a
    new image can be obtained, which provides the most detailed, reliable, and
    discriminating information. In this method fused images are generated using
    visual and thermal face images in the first step. In the second step, fused
    images are projected into eigenspace and finally classified using a radial
    basis function neural network.

  2. A Parallel Framework for Multilayer Perceptron for Human Face Recognition.

    Tue, 07/06/2010 - 15:03 — SomNambul
    Authors: Debotosh Bhattacharjee, M. Kundu, D. K. Basu, M. Nasipuri, M.K. Bhowmik
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    Artificial neural networks have already shown their success in face
    recognition and similar complex pattern recognition tasks. However, a major
    disadvantage of the technique is that it is extremely slow during training for
    larger classes and hence not suitable for real-time complex problems such as
    pattern recognition. This is an attempt to develop a parallel framework for the
    training algorithm of a perceptron. In this paper, two general architectures
    for a Multilayer Perceptron (MLP) have been demonstrated.

  3. Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human Face Recognition - A Comparative Study.

    Tue, 07/06/2010 - 15:03 — SomNambul
    Authors: Debotosh Bhattacharjee, M. Kundu, D. K. Basu, M. Nasipuri, M. K. Bhowmik
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    In this paper we present a comparative study on fusion of visual and thermal
    images using different wavelet transformations. Here, coefficients of discrete
    wavelet transforms from both visual and thermal images are computed separately
    and combined. Next, inverse discrete wavelet transformation is taken in order
    to obtain fused face image. Both Haar and Daubechies (db2) wavelet transforms
    have been used to compare recognition results. For experiments IRIS
    Thermal/Visual Face Database was used.

  4. Fuzzy Classification of Facial Component Parameters.

    Thu, 07/01/2010 - 15:02 — SomNambul
    Authors: M. Kundu, D. K. Basu, M. Nasipuri, S. Halder, Debotosh Bhattacherjee
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    This paper presents a novel type-2 Fuzzy logic System to define the Shape of
    a facial component with the crisp output. This work is the part of our main
    research effort to design a system (called FASY) which offers a novel face
    construction approach based on the textual description and also extracts and
    analyzes the facial components from a face image by an efficient technique. The
    Fuzzy model, designed in this paper, takes crisp value of width and height of a
    facial component and produces the crisp value of Shape for different facial
    components.

  5. Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance.

    Thu, 07/01/2010 - 15:02 — SomNambul
    Authors: Mita Nasipuri, Sandhya Arora, Debotosh Bhattacharjee, M. Kundu, D. K. Basu
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    This paper deals with a new method for recognition of offline Handwritten
    non-compound Devnagari Characters in two stages. It uses two well known and
    established pattern recognition techniques: one using neural networks and the
    other one using minimum edit distance. Each of these techniques is applied on
    different sets of characters for recognition. In the first stage, two sets of
    features are computed and two classifiers are applied to get higher recognition
    accuracy. Two MLP's are used separately to recognize the characters.

  6. Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition.

    Thu, 07/01/2010 - 15:01 — SomNambul
    Authors: Mita Nasipuri, Sandhya Arora, Debotosh Bhattacharjee, L. Malik, M. Kundu, D. K. Basu
    Subjects: Computer Vision and Pattern Recognition
    Abstract

    Classification methods based on learning from examples have been widely
    applied to character recognition from the 1990s and have brought forth
    significant improvements of recognition accuracies. This class of methods
    includes statistical methods, artificial neural networks, support vector
    machines (SVM), multiple classifier combination, etc.

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