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Gaurav Sharma
Researcher at Shenzhen University
Publications - 1520
Citations - 40824
Gaurav Sharma is an academic researcher from Shenzhen University. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 82, co-authored 1244 publications receiving 31482 citations. Previous affiliations of Gaurav Sharma include Northeastern University & D. E. Shaw & Co..
Papers
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Journal ArticleDOI
The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations
TL;DR: This article indicates several potential implementation errors that are not uncovered in tests performed using the original sample data published with the recently developed CIEDE2000 color-difference formula.
Proceedings ArticleDOI
Reversible data hiding
TL;DR: A prediction-based conditional entropy coder which utilizes static portions of the host as side-information improves the compression efficiency, and thus the lossless data embedding capacity.
Journal ArticleDOI
Lossless generalized-LSB data embedding
TL;DR: In this paper, a generalization of the well-known least significant bit (LSB) modification is proposed as the data-embedding method, which introduces additional operating points on the capacity-distortion curve.
BookDOI
Digital Color Imaging Handbook
TL;DR: In this paper, Sharma et al. present a color transformation implementation for digital cameras. But they do not discuss the use of color hightones for digital image processing, instead they focus on a human visual model based color quantization.
Journal ArticleDOI
Multiple Wearable Sensors in Parkinson and Huntington Disease Individuals: A Pilot Study in Clinic and at Home
Jamie L. Adams,Karthik Dinesh,Mulin Xiong,Christopher G. Tarolli,Saloni Sharma,Nirav Sheth,Alexander J. Aranyosi,William Zhu,Steven Goldenthal,Kevin M. Biglan,E. Ray Dorsey,Gaurav Sharma +11 more
TL;DR: Among individuals with movement disorders, the use of wearable sensors in clinic and at home was feasible and well-received, and these sensors can identify statistically significant differences in activity profiles between individuals with movements disorders and those without.