V. P. Shukla

Work place: FET, Mody Institute of Technology & Science, Laxmangarh, India

E-mail: drsvprasad2k@yahoo.com


Research Interests: Cellular Automata, Image Processing, Image Manipulation, Image Compression, Computer Animation, Computer Vision


Vidya Prasad Shukla was born in India, in 1954. He received his M.Sc. (Applied Mathematics) in 1976, Ph.D. (Modelling and Computer Simulation) in 1982 and PG Dip. (Computational Hydraulic Engineering) in 1986 from Avadh University Faizabad, Indian Institute of Technology Kanpur and International Institute of Environmental & Hudraulic Engineering (Delft) the Netherlands respectively. He worked and officiated at various posts as Senior Research Officer, Chief Research Officer and HOD Computer Division at from Central Water and Power research Station (CWPRS), Pune from 1982 to 2003. Thereafter, he worked as a Professor in BIT, Sathyamangalam and NIT Durgapur. He has joined as a Professor in Mody Institute of Technology & Science, Deemed University Laxmangarh in 2009. He has published over 57 papers in refereed journals and conference proceedings and written 29 technical reports on various clients sponsored research projects of international/national importance. He is an editor of the book ―Development of Coastal Engineering‖ from CWPRS, Pune. His current research interest includes Computer Simulation & Modelling, Image processing, Cellular Automata, Soft-Computing, Computer Vision, Nanotech-simulation, Operations Research, Mathematical Biology, Modelling of Arms Race of Nations.

Author Articles
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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Performance Analysis of Texture Image Classification Using Wavelet Feature

By Dolly Choudhary Ajay Kumar Singh Shamik Tiwari V. P. Shukla

DOI: https://doi.org/10.5815/ijigsp.2013.01.08, Pub. Date: 8 Jan. 2013

This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naïve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.

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