Uttam D.Kolekar

Work place: A.P. Shah Institute of Technology, Thane, India

E-mail: uttamkolekar@gmail.com


Research Interests: Image Processing, Image Manipulation, Image Compression


Mr. Omprakash S. Rajankar has received M.E. (Electronics Engineering) degree from SRTM University, Nanded in 2010. Currently pursuing Ph.D. from NMIMS University. He has 24 years of experience in teaching. Currently he is working as Principal at Zeal Polytechnic, Pune. His areas of interest are Image Processing and VLSI. He is a member of IEEE and life member of ISTE(India). He has 6 papers in International Conferences/journal to his credit.

Author Articles
Effect of Single and Multiple ROI Coding on JPEG2000 Performance

By Omprakash S. Rajankar Uttam D.Kolekar

DOI: https://doi.org/10.5815/ijigsp.2016.04.04, Pub. Date: 8 Apr. 2016

Images are an integral part of advertisements. Images make the web pages heavy. It increases the response time if the size of the image is large and or available bandwidth is low. The consequence of it is viewer may lose his interest in the particular advertisement if he has to wait for a longer time. Image compression is one of the solutions to this problem. In advertisement images, ROI is of prime importance. Though the context of ROI and background regions are not of prime importance, they cannot be totally discarded. This paper investigates the effect of ROI coding on JPEG2000 performance. It proposes Multiple ROI (MROI) coding for compression of natural and advertisement images at moderate compression ratio. The proposed MROI coding prioritizes ROI codeblocks according to the ROI importance, and contribution of ROI in the specific ROI codeblock. It improves fine-grain accuracy at codeblock level also efficiently utilize the given bit budget with a negligible increase in encoding time. 

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Scale Space Reduction with Interpolation to Speed up Visual Saliency Detection

By Omprakash S. Rajankar Uttam D.Kolekar

DOI: https://doi.org/10.5815/ijigsp.2015.08.07, Pub. Date: 8 Jul. 2015

The scale of salient object in an image is not a known priori, therefore to detect salient objects accurately multiple scale analysis is used by saliency detection models. However, multiple scale analysis makes the saliency detection slow. Fast and accurate saliency detection is essential to obtain Region of Interest in image processing applications. This paper proposes a scale space reduction with interpolation to speed up the saliency detection. To demonstrate the concept, this method is integrated with Hypercomplex Fourier Transform saliency detection which reduced the computational complexity from O(N) to O(N/2).

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