Dr. Sourabh Sharma
@rtu.ac.in
Associate Professor Department of Computer Science and Engineering
Avantika University
EDUCATION
Ph.D. Computer Science & Engineering
M.Tech. Computer Science & Engineering
B.E. Computer Science & Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Deepak Kumar Jain, Xudong Zhao, Chenquan Gan, Piyush Kumar Shukla, Amar Jain, and Sourabh Sharma
Elsevier BV
Sourabh Sharma, Harish Sharma, Janki Ballabh Sharma, and Ramesh Chandra Poonia
Springer Science and Business Media LLC
Tinu Kumar, Bobbinpreet Kaur, Sourabh Sharma, and Sheenam
IEEE
This term paper looks at vehicle location procedures that can be utilized for activity observing frameworks. The framework works in conjunction with CCTV camera integration to identify vehicles. The first step is always automatic object detection. Hair cascade is used to detect cars in video. We train these cascade classifiers using the Viola-Jones algorithm. Adjust to discover special objects in recordings by following each car in a chosen locale of intrigued. It is one of the speediest ways to precisely distinguish, track and check car objects with up to 78% precision.
Dibakar Sinha, Ashish Sharma, and Sourabh Sharma
SPIE-Intl Soc Optical Eng
Abstract. Congenital heart failure (CHF) due to congestion in the blood is a serious cardiac problem correlated with crippling symptoms and leading to a rising death rate, monumental health care spending, and reduced quality of life. Heart disease prevention is among the most crucial functions of any medical system, as many people are prone to heart attacks worldwide. Although several segmentation methods for great vessels and the heart have been proposed in the research, they are not successful when applied to the health records of congenital heart disease. In this proposed work, the thickness and fat accumulation of most arteries are measured and analyzed, and then the measurement is synthesized with the corresponding width of the blood vessels of arteries; this data is used for training purposes in the convolutional neural network with one-off cross-validation and regularization. Using the CNN model, a confusion matrix is created and different statistical parameters such as accuracy sensitivity, specificity, precision, and f-score are generated. The final average accuracy was 97%, precision was 98.13%, and F-score was 98.36%. The results indicate that the CNN-based strategy can distinguish healthy hearts from those with prior cardiovascular disease.
Sourabh Sharma, Harish Sharma, and Janki Ballabh Sharma
Springer Science and Business Media LLC
Sourabh Sharma, Harish Sharma, and Janki Ballabh Sharma
Elsevier BV
Abstract Image watermarking has emerged as a useful method for solving security issues like authenticity, copyright protection and rightful ownership of digital data. Existing watermarking schemes use either a binary or grayscale image as a watermark. This paper proposes a new robust and adaptive watermarking scheme in which both the host and watermark are the color images of the same size and dimension. The security of the proposed watermarking scheme is enhanced by scrambling both color host and watermark images using Arnold chaotic map. The host image is decomposed by redundant discrete wavelet transform (RDWT) into four sub-bands of the same dimension, and then approximate sub-band undergoes singular value decomposition (SVD) to obtain the principal component (PC). The scrambled watermark is then directly inserted into a principal component of scrambled host image, using an artificial bee colony optimized adaptive multi-scaling factor, obtained by considering both the host and watermark image perceptual quality to overcome the tradeoff between imperceptibility and robustness of the watermarked image. The hybridization of RDWT-SVD provides an advantage of no shift-invariant to achieve higher embedding capacity in the host image and preserving the imperceptibility and robustness by exploiting SVD properties. To measure the imperceptibility and robustness of the proposed scheme, both qualitative and quantitative evaluation parameters like peak signal to noise ratio (PSNR), structural similarity index metric (SSIM) and normalized cross-correlation (NC) are used. Experiments are performed against several image processing attacks and the results are analyzed and compared with other related existing watermarking schemes which clearly depict the usefulness of the proposed scheme. At the same time, the proposed scheme overcomes the major security problem of false positive error (FPE) that mostly occurs in existing SVD based watermarking schemes.