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Articles by R. Krishnamoorthy
Total Records ( 6 ) for R. Krishnamoorthy
  R. Krishnamoorthy and M. Ganesh
  In this study, a simple technique for defect identification based on Orthogonal Polynomials (OP) model is presented. Initially, the input image under analysis is applied with OP model and gradient estimation scheme is employed to locate the edges present. The resulting binary image is again applied with OP model, and a simple computation scheme that finds the ratio between selected transform coefficients is proposed to identity the defects present in the image. Experiments have been conducted with different images consisting of both homogeneous and non homogeneous regions. The proposed technique is found to perform well, for unshaped defects, and is found to outperform the existing schemes.
  R. Krishnamoorthy and M. Braveen
  In this study, a new optimal feature selection scheme with orthogonal polynomials and Ant Colony Optimization (ACO) for Content-Based Video Retrieval System (CBVRS) is proposed. Initially, the video file is divided in to smaller number of chunks as shots in orthogonal polynomials transform domain. In order to identify the key frames to represent a shot, each video image inside a shot is then applied with same orthogonal polynomials to yield Direct Coefficients (DC) images. In this research, the DC image which has the maximum DC value is modeled to be a key frame. From the identified key frames, low level feature such as color, edge and texture information are extracted in the same orthogonal polynomials domain. Since, the extracted features are larger in size, ACO scheme is adopted to select optimal features that represent a key frame for content-based video retrieval system.
  R. Krishnamoorthy and K.R. Suneetha
  User interest estimation is an imperative part of any personalized information retrieval system whose quality is decided based on the accuracy of user interest description. This study proposes a Behavior Monitoring Measurement System which is an advanced metric model for estimating the degree of user’s interest based on his browsing behaviors and histories. Since, browsing behaviors are the valuable indicators of user’s interest, this metric considers browsing behaviors such as duration of time spent in browsing each page and various other user activities. As the user behavior changes the measurement values would get updated and finally degree of satisfaction would be estimated. The calculation for the degree of the matic interest is based on Clustering Rule Method. The mathematical formulation for calculating the interested degree is also presented in this study. The experimental results confirm that the proposed system reflects user's satisfaction degree accurately and dynamically.
  J. Senthilkumar , D. Manjula , A. Kannan and R. Krishnamoorthy
  In this study, researchers propose a novel Automatic Supervised Feature Selection and Discretization algorithm to enhance the classification of medical images (mammograms). The proposed method consists of a new algorithm called, NANO for a filter based supervised feature selection and discretization. This algorithm solves two problems, viz., feature discretization and selection in a single step. An important contribution of the proposed algorithm is the reduction of irrelevant items to be mined. NANO selects the relevant features based on the average global inconsistency and average global cut point measures, speeding up the medical image diagnosis framework. Two set of experiments have been performed to validate the proposed method. Experiments are carried out to validate the performance of NANO algorithm in the task of feature selection and discretization. Performance evaluation was done for the first experiments using precision and recall metrics obtained from the query and retrieved images. The second set of experiments aim at validating the classification accuracy. From the experiments, it is observed that the proposed method shows high sensitivity (up to 98.64%) and high accuracy (up to 96.95%).
  R. Krishnamoorthy and R. Bhavani
  A new computational model based face recognition system with edge extraction scheme is presented in this research. The proposed model has been built centering on some simple point spread operators, which are easily constructed from a set of orthogonal polynomials. One speciality of these point-spread operators is that they can be used in transforming vis-à-vis approximating 2D monochrome image regions. Also a complete set of difference operators are configured from these point-spread operators. Initially, we detect the face from the given input image using an edge extraction scheme, derived as maximizing the signal to noise ratio due to operator’s response supported by the proposed orthogonal polynomials. Simple procedures are derived to compute characteristic subsets of coefficients of the proposed transformation that represent important features, are considered for face recognition on the face detected input image. The proposed face recognition system is tested with The Yale database and also compared with Discrete Cosine Transform based face recognition system, Principle Component Analysis based face recognition system and fisher face recognition system.
  R. Krishnamoorthy and K. Selvakumar
  In this research a new coding technique for Space Time Block Code (STBC) in terms of code operator, that could effectively handle the multipath fading (over rayleigh fading channel) is proposed. The proposed coding technique has been built around a set of carefully chosen orthogonal polynomials. The proposed coding scheme exploits the maximum diversity order for a given number of transmit and receive antennas subject to the constraints of having a simple decoding algorithm. The proposed scheme is similar to the generalized STBC. In the simulation work Phase Shift Key (PSK) and Quadrature Amplitude Shift Key (QASK) are used and perfect channel knowledge is assumed. At the receiver end, we use Maximum Likelihood (ML) decoding. This proposed coding technique results in a full diversity code with high coding advantage.
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