Banshidhar Majhi

National Institute of Technology Rourkela, Sundergarh, Orissa, India

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Publications (98)18.49 Total impact

  • Sambit Bakshi, Pankaj K. Sa, Banshidhar Majhi
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    ABSTRACT: The article proposes a novel multi-scale local feature based on the periocular recognition technique which is capable of extracting high-dimensional subtle features existent in the iris region as well as low-dimensional gross features in the periphery skin region of the iris. A set of filter banks of different scales is employed to exploit the phase-intensive patterns in visible spectrum periocular image of a subject captured from a distance in partial non-cooperative scenario. The proposed technique is verified with experiments on near-infrared illumination databases like BATH and CASIA-IrisV3-Lamp. Experiments have been further extended to images from visible spectrum ocular databases like UBIRISv2 and low-resolution eye regions extracted from FERETv4 face database to establish that the proposed feature performs comparably better than existing local features. To find the robustness of the proposed approach, the low resolution visible spectrum images of mentioned databases are converted to grayscale images. The proposed approach yields unique patterns from these grayscale images. The ability to find coarse-to-fine features in multi-scale and different phases is accountable for the improved robustness of the proposed approach.
    Biocybernetics and Biomedical Engineering 06/2014; · 0.16 Impact Factor
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    ABSTRACT: In this paper we propose a hybrid computational paradigm associated with cloud computing and wireless sensor network. Attempts have been made to devise a protocol that establishes communication between sensors and cloud resources. The objective is to increase the availability of real time sensor information across the users. Further it will be possible to share the information of various application specific sensor network deployed at various locations. We suggest a virtualisation for sensor vs. cloud and cloud vs. users. This will facilitate the user to visualise sensor network and cloud.
    International Journal of Information and Communication Technology 04/2014; 6(2):156-174.
  • Ratnakar Dash, Banshidhar Majhi
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    ABSTRACT: This paper deals with estimation of parameters for motion blurred images. The objectives are to estimate the length (L) and the blur angle (θ) of the given degraded image as accurately as possible so that the restoration performance can be optimised. Gabor filter is utilized to estimate the blur angle whereas a trained radial basis function neural network (RBFNN) estimates the blur length. Once these parameters are estimated the conventional restoration is performed. To validate the proposed scheme, simulation has been carried out on standard images as well as in real images subjected to different blur angles and lengths. The robustness of the scheme is also validated in noise situations of different strengths. In all situations, the results have been compared with standard schemes. It is in general observed that the proposed scheme outperforms its counterparts in terms of restoration parameters and visual quality.
    Optik - International Journal for Light and Electron Optics 03/2014; 125(5):1634–1640. · 0.77 Impact Factor
  • Suvendu Rup, Banshidhar Majhi, Sudarshan Padhy
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    ABSTRACT: This paper presents a side information (SI) scheme for distributed video coding based on multilayer perceptron. The suggested scheme predicts a Wyner–Ziv (WZ) frame from two decoded key frames. The network is trained offline using patterns from different standard video sequences with varied motion characteristics to achieve generalization. The proposed scheme is simulated along with other standard video coding schemes. Performance comparisons have been made with respect to training convergence, rate distortion (RD), peak signal to noise ratio (PSNR), number of requests per SI frame, decoding time requirement, etc. In general, it is observed that the proposed scheme has a superior SI frame generation capability as compared to its competent schemes.
    AEU - International Journal of Electronics and Communications 03/2014; 68(3):201–209. · 0.70 Impact Factor
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    ABSTRACT: Human recognition is an essential requirement for human-centric surveillance, activity recognition, gait recognition etc. Inaccurate recognition of humans in such applications may leads to false alarm and unnecessary computation. In the proposed work a robust background modeling algorithm using fuzzy logic is used to detect foreground objects. Three distinct features are extracted from the contours of detected objects. An unique aggregated feature vector is formed using a fuzzy inference system by aggregating three feature vectors. To minimize computation in recognition using Hidden Markov model (HMM), the length of final feature vector is reduced using vector quantization. The proposed method is explained using five basic phases; background modeling and foreground object detection, features extraction, aggregated feature vector calculation, vector quantization, and recognition using Hidden Markov model.
    AEU - International Journal of Electronics and Communications 03/2014; 68(3):227–236. · 0.70 Impact Factor
  • Bibekananda Jena, Punyaban Patel, Banshidhar Majhi
    Elixir International Journal. 01/2014; 71:24729-24734.
  • Source
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    ABSTRACT: Iris as a biometric identifier is assumed to be stable over a period of time. However, some researchers have observed that for long time lapse, the genuine match score distribution shifts towards the impostor score distribution and the performance of iris recognition reduces. The main purpose of this study is to determine if the shift in genuine scores can be attributed to aging or not. The experiments are performed on the two publicly available iris aging databases namely, ND-Iris-Template-Aging-2008-2010 and ND-TimeLapseIris-2012 using a commercial matcher, VeriEye. While existing results are correct about increase in false rejection over time, we observe that it is primarily due to the presence of other covariates such as blur, noise, occlusion, and pupil dilation. This claim is substantiated with quality score comparison of the gallery and probe pairs.
    PLoS ONE 11/2013; 8(11):e78333. · 3.53 Impact Factor
  • International Journal Of Research In Computer Applications And Robotics, ISSN 2320-7345. 11/2013; 01(08):27-33.
  • Hunny Mehrotra, Banshidhar Majhi
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    ABSTRACT: This paper proposes an efficient retrieval approach for iris using local features. The features are extracted from segmented iris image using scale invariant feature transform (SIFT). The keypoint descriptors extracted from SIFT are clustered into m groups using k-means. The idea is to perform indexing of keypoints based on descriptor property. During database indexing phase, k-d tree k-dimensional tree is constructed for each cluster center taken from N iris images. Thus for m clusters, m such k-d trees are generated denoted as t i , where 1 ¿ i ¿ m. During the retrieval phase, the keypoint descriptors from probe iris image are clustered into m groups and ith cluster center is used to traverse corresponding t i for searching. k nearest neighbor approach is used, which finds p neighbors from each tree (t i ) that falls within certain radius r centered on the probe point in k-dimensional space. Finally, p neighbors from m trees are combined using union operation and top S matches (S ⊆ (m× p)) corresponding to query iris image are retrieved. The proposed approach has been tested on publicly available databases and outperforms the existing approaches in terms of speed and accuracy.
    Frontiers of Computer Science (print) 10/2013; 7(5):767-781. · 0.41 Impact Factor
  • K.K. Hati, P.K. Sa, B. Majhi
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    ABSTRACT: In this letter, we propose an intensity range based object detection scheme for videos with fixed background and static cameras. The scheme suggests two different algorithms; the first one models the background from initial few frames and the second algorithm extracts the objects based on local thresholding. The strength of the scheme lies in its simplicity and the fact that, it defines an intensity range for each pixel location in the background to accommodate illumination variation as well as motion in the background. The efficacy of the scheme is shown through comparative analysis with competitive methods. Both visual as well as quantitative measures show an improved performance and the scheme has a strong potential for applications in real time surveillance.
    IEEE Signal Processing Letters 08/2013; 20(8):759-762. · 1.64 Impact Factor
  • Hunny Mehrotra, Banshidhar Majhi
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    ABSTRACT: In this paper, an indexing approach is proposed for clustered SIFT keypoints using k-d-b tree. K-d-b tree combines the multidimensional capability of k-d tree and balancing efficiency of B tree. During indexing phase, each cluster center is used to traverse the region pages of k-d-b tree to reach an appropriate point page for insertion. For m cluster centers, m such trees are constructed. Insertion of a node into k-d-b tree is dynamic that generates balanced data structure and incorporates deduplication check as well. For retrieval, range search approach is used which finds the intersection of probe cluster center with each region page being traversed. The iris identifiers on the point page referenced by probe iris image are retrieved. Results are obtained on publicly available BATH and CASIA Iris Image Database Version 3.0. Empirically it is found that k-d-b tree is preferred over state-of-the-art biometric database indexing approaches.
    Proceedings of the 9th international conference on Intelligent Computing Theories and Technology; 07/2013
  • Hunny Mehrotra, Pankaj K. Sa, Banshidhar Majhi
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    ABSTRACT: In this paper a robust segmentation and an adaptive SURF descriptor are proposed for iris recognition. Conventional recognition systems extract global features from the iris. However, global features are subject to change for transformation, occlusion and non-uniform illumination. The proposed iris recognition system handles these issues. The input iris image is used to remove specular highlights using an adaptive threshold. Further, the pupil and iris boundaries are localized using a spectrum image based approach. The annular region between the pupil and iris boundaries is transformed into an adaptive strip. The strip is enhanced using a gamma correction approach. Features are extracted from the adaptive strip using Speeded Up Robust Features (SURF). The results obtained using SURF are compared with the existing SIFT descriptor and the proposed approach performs with improved accuracy and reduced computation cost.
    Mathematical and Computer Modelling 07/2013; 58(s 1–2):132–146. · 2.02 Impact Factor
  • Sambit Bakshi, Hunny Mehrotra, Banshidhar Majhi
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    ABSTRACT: This article looks into pros and cons of the conventional global and local feature matching techniques for iris. The review of related research works on matching techniques leads to the observation that local features like scale invariant feature transform SIFT gives satisfactory recognition accuracy for good quality images. However the performance degrades when the images are occluded or taken non-cooperatively. As SIFT matches keypoints on the basis of 128-D local descriptors, hence it sometimes falsely pairs two keypoints which are from different portions of two iris images. Subsequently the need for filtering or pruning of faulty SIFT pairs is felt. The paper proposes two methods of filtering impairments faulty pairs based on the knowledge of spatial information of the keypoints. The two proposed pruning algorithms angular filtering and scale filtering are applied separately and applied in union to have a complete comparative analysis of the result of matching. The pruning approaches has given better recognition accuracy than conventional SIFT when experimented on two publicly available BATH and CASIAv3 iris databases.
    International Journal of Biometrics 03/2013; 5(2):160-180.
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    Sambit Bakshi, Pankaj K Sa, Banshidhar Majhi
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    ABSTRACT: A novel approach for selecting a rectangular template around periocular region optimally potential for human recognition is proposed. A comparatively larger template of periocular image than the optimal one can be slightly more potent for recognition, but the larger template heavily slows down the biometric system by making feature extraction computationally intensive and increasing the database size. A smaller template, on the contrary, cannot yield desirable recognition though the smaller template performs faster due to low computation for feature extraction. These two contradictory objectives (namely, (a) to minimize the size of periocular template and (b) to maximize the recognition through the template) are aimed to be optimized through the proposed research. This paper proposes four different approaches for dynamic optimal template selection from periocular region. The proposed methods are tested on publicly available unconstrained UBIRISv2 and FERET databases and satisfactory results have been achieved. Thus obtained template can be used for recognition of individuals in an organization and can be generalized to recognize every citizen of a nation.
    BioMed research international. 01/2013; 2013:481431.
  • A. Kumar, B. Majhi
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    ABSTRACT: Iris is the Optimum Biometric-trait present in Biometrics Security. Our emphasis on this paper is to obtain efficient, fast and robust algorithm set for iris detection. There are number of algorithms proposed for the efficient result but fails due to limitations. We tried in this paper to make, an efficient combination of the best schemes of normalization, corner detection, feature extraction and best matching algorithm available. We have used Modified-Trajkovic Operator (8-neighbours) to detect the corner, in which, the iris image sample is first optimized by sector based normalization into four sectors, this decreases the iris area but the modified 8-neighbours detect the corner accurately. Fourier-SIFT is then used to determine the keypixels with enhanced threshold cutoff and finally Modified-Hausdorff distance (or Gromov-Hausdorff distance) determines the matching algorithm and measures the distance between keypixels of enrolled and scanned iris during matching. The limitations in the above algorithms are rectified in this paper. This process is rigorously checked on CASIA-V3and MMU iris images.
    Communications and Signal Processing (ICCSP), 2013 International Conference on; 01/2013
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    ABSTRACT: This paper presents a scale and view invariant approach for human detection in the presence of various other objects like animals, vehicles, etc. Human detection is one of the essential steps in applications like activity recognition, gait recognition, human centric surveillance etc. Inaccurate detection of humans in such applications may increase the number of false alarms. In the proposed work, fuzzy logic has been used to model a robust background for object detection. Three different features are extracted from the contours of the detected objects. These features are aggregated using fuzzy inference system. Then human contour is identified using template matching. The proposed method consists of four main steps; Moving Object Detection, Feature Extraction, Feature Aggregation, and Human Contour Detection.
    Fuzzy Systems (FUZZ), 2013 IEEE International Conference on; 01/2013
  • P.K. Sa, B. Majhi
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    ABSTRACT: The point spread functions (PSF) responsible for degrading the observed images are very often not known. Hence, the image must be restored only from the available noisy blurred observation. This paper proposes two new image restoration algorithms, which are based on support vector regression (SVR). The first algorithm uses local variance and the second algorithm utilizes the concepts of fuzzy systems to counter blur in a given image. These algorithms significantly reduce the training time through their effective sample selection mechanisms. Experimental findings show that the proposed techniques deliver superior results for a variety of blurs and PSFs.
    Fuzzy Systems (FUZZ), 2013 IEEE International Conference on; 01/2013
  • T.K. Mishra, B. Majhi, S. Panda
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    ABSTRACT: The work proposed in this paper is an attempt to develop two recognizers for Odia handwriting based on basic transformation schemes and compares their pros and cons. Both the recognizers put emphasis on exploiting the inherent characteristics of the Odia numeral images. The proposed method analyzes the use of Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) for this purpose. Recognition by classification is achieved by feeding these vectors as input to a Back Propagation Neural Network (BPNN). Recognition results are obtained and compared by experimentally varying the classifier parameters. Our experimental results come out to be promising. Thus, finally, we come of with a robust recognizer for handwritten Odia numerals.
    Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on; 01/2013
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    ABSTRACT: This paper proposes a novel adaptive filtering scheme to remove impulse noise from images. The scheme replaces the corrupted test pixel with the median value of non-corrupted neighboring pixels selected from a window dynamically. If the number of non-corrupted pixels in the selected window is not sufficient, a window of next higher size is chosen. Thus window size is automatically adapted based on the density of noise in the image as well as the density of corruption local to a window. As a result window size may vary pixel to pixel while filtering. The scheme is simple to implement and do not require multiple iterations. The efficacy of the proposed scheme is evaluated with respect to subjective as well as objective parameters on standard images on various noise densities. Comparative analysis reveals that the proposed scheme has improved performance over other schemes, preferably in high density impulse noise cases. Further, the computational overhead is also less as compared its competent scheme.
    International Journal Image, Graphics and Signal Processing. 09/2012; 11:53-62.
  • Ratnakar Dash, Pankaj Kumar Sa, Banshidhar Majhi
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    ABSTRACT: This paper presents a swarm intelligence based parameter optimization of the support vector machine (SVM) for blind image restoration. In this work, SVM is used to solve a regression problem. Support vector regression (SVR) has been utilized to obtain a true mapping of images from the observed noisy blurred images. The parameters of SVR are optimized through particle swarm optimization (PSO) technique. The restoration error function has been utilized as the fitness function for PSO. The suggested scheme tries to adapt the SVM parameters depending on the type of blur and noise strength and the experimental results validate its effectiveness. The results show that the parameter optimization of the SVR model gives better performance than conventional SVR model as well as other competent schemes for blind image restoration.
    Journal of Computer Science and Technology 09/2012; 27(5). · 0.64 Impact Factor

Publication Stats

166 Citations
18.49 Total Impact Points

Institutions

  • 2006–2014
    • National Institute of Technology Rourkela
      • Department of Computer Science and Engineering (CS)
      Sundergarh, Orissa, India
  • 2011
    • King Khalid University
      Ebha, Minţaqat ‘Asīr, Saudi Arabia
  • 2010
    • National Institute of Technology, Warangal
      • School of Management
      Warangal, Andhra Pradesh, India