S. R. Biradar

Work place: SDM College of Engineering, Hubli-Dharwad, India

E-mail: srbiradar@gmail.com


Research Interests: Image Processing, Image Manipulation, Image Compression, Computer systems and computational processes


Mr. S.R. Biradar is a Professor in the department of Computer Science and Engineering, SDM, Dharwad, India. He received his B.E, M.Tech and Ph.D. degrees in Computer Science and Engineering from Karnataka University, MAHE Manipal and Jadavpur University respectively. His research interest includes Mobile Ad-hoc networking, advanced wireless communication. He has published over 45 papers in refereed journals and conference proceedings. His current research interest includes Image Processing, Mobile ad hoc networks, and sensor networks.

Author Articles
Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects

By Ajay Kumar Singh V. P. Shukla Shamik Tiwari S. R. Biradar

DOI: https://doi.org/10.5815/ijisa.2015.03.07, Pub. Date: 8 Feb. 2015

Computer vision applications face various challenges while detection and classification of objects in real world like large variation in appearances, cluttered back ground, noise, occlusion, low illumination etc.. In this paper a Wavelet based Histogram of Oriented Gradients (WHOG) feature descriptors are proposed to represent shape information by storing local gradients in image. This results in enhanced representation of shape information. The performance of the feature descriptors are tested on multiclass image data set having partial occlusion, different scales and rotated object images. The performance of WHOG feature based object classification is compared with HOG feature based classification. The matching of test image with its learned class is performed using Back Propagation Neural Network (BPNN) algorithm. Proposed features not only performed superior than HOG but also beat wavelet, moment invariant and Curvelet.

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Blur Classification using Ridgelet Transform and Feed Forward Neural Network

By Shamik Tiwari V. P. Shukla S. R. Biradar A. K. Singh

DOI: https://doi.org/10.5815/ijigsp.2014.09.06, Pub. Date: 8 Aug. 2014

The objective of image restoration approach is to recover a true image from a degraded version. This problem can be stated as blind or non-blind depending upon whether blur parameters are known prior to the restoration process. Blind restoration deals with parameter identification before deconvolution. Though there exists multiple blind restorations techniques but blur type recognition is extremely desirable before application of any blur parameters estimation approach. In this paper, we develop a blur classification approach that deploys a feed forward neural network to categories motion, defocus and combined blur types. The features deployed for designing of classification system include mean and standard deviation of ridgelet energies. Our simulation results show the preciseness of proposed method.

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Wavelet Based Intentional Blurring Variance Scheme for Blur Detection in Barcode Images

By Shamik Tiwari V. P. Shukla S. R. Biradar Ajay Kumar Singh

DOI: https://doi.org/10.5815/ijigsp.2014.06.06, Pub. Date: 8 May 2014

Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. Automatic blur detection is extremely enviable to restore barcode image or simply utilize them. That is to assess whether a given image is blurred or not. To detect blur, many algorithms have been proposed. These algorithms are different in their performance, time complexity, precision, and robustness in noisy environments. In this paper, we present an efficient method blur detection in barcode images, with no reference perceptual blur metric using wavelets.

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Blur Classification Using Wavelet Transform and Feed Forward Neural Network

By Shamik Tiwari V. P. Shukla S. R. Biradar A. K. Singh

DOI: https://doi.org/10.5815/ijmecs.2014.04.03, Pub. Date: 8 Apr. 2014

Image restoration deals with recovery of a sharp image from a blurred version. This approach can be defined as blind or non-blind based on the availability of blur parameters for deconvolution. In case of blind restoration of image, blur classification is extremely desirable before application of any blur parameters identification scheme. A novel approach for blur classification is presented in the paper. This work utilizes the appearance of blur patterns in frequency domain. These features are extracted in wavelet domain and a feed forward neural network is designed with these features. The simulation results illustrate the high efficiency of our algorithm.

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Enhanced Performance of Multi Class Classification of Anonymous Noisy Images

By Ajay Kumar Singh V. P. Shukla S. R. Biradar Shamik Tiwari

DOI: https://doi.org/10.5815/ijigsp.2014.03.04, Pub. Date: 8 Feb. 2014

An important constituents for image classification is the identification of significant characterstics about the specific class to distinguish intra class variations. Since performance of the classifiers is affected in the presence of noise, so selection of discriminative features is an important phase in classification. This superfluous information i.e. noise, e.g. additive noise may occur in images due to image sensors i.e. of the constant noise level in dark areas of the image or salt & pepper noise may be caused by analog to digitals conversion and bit error transmission etc.. Detection of noise is also very essential in the images for choosing appropriate filter. This paper presents an experimental assessment of neural classifier in terms of classification accuracy under three different constraints of images without noise, in presence of unknown noise and after elimination of noise.

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