R
Rajeev Sharma
Researcher at Pennsylvania State University
Publications - 108
Citations - 5592
Rajeev Sharma is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Gesture & Gesture recognition. The author has an hindex of 34, co-authored 107 publications receiving 5446 citations. Previous affiliations of Rajeev Sharma include University of Illinois at Urbana–Champaign.
Papers
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Journal ArticleDOI
Motion perceptibility and its application to active vision-based servo control
Rajeev Sharma,Seth Hutchinson +1 more
TL;DR: A quantitative measure of motion perceptibility is derived, which relates the magnitude of the rate of change in an object's position to the magnitude in the image of that object, and is combined with the traditional notion of manipulability, into a composite perceptibility/manipulability measure.
Journal ArticleDOI
Adaptive texture and color segmentation for tracking moving objects
TL;DR: This paper presents a formulation for fusing texture and color in a manner that makes the segmentation reliable while keeping the computational cost low, with the goal of real-time target tracking.
Journal ArticleDOI
On motion planning in changing, partially predictable environments
Steven M. LaValle,Rajeev Sharma +1 more
TL;DR: A framework for analyzing and computing motion plans for a robot that operates in an environment that both varies over time and is not completely predictable is presented, which leads to a dynamic programming- based algorithm for determining optimal strategies.
Journal ArticleDOI
Speech/gesture interface to a visual-computing environment
Rajeev Sharma,M. Zeller,Vladimir Pavlovic,Thomas S. Huang,Z. Lo,Stephen M. Chu,Yunxin Zhao,James C. Phillips,Klaus Schulten +8 more
TL;DR: A speech/gesture interface that uses visual hand-gesture analysis and speech recognition to control a 3D display in VMD, a virtual environment for structural biology, to simplify model manipulation and rendering to make biomolecular modeling more playful.
Patent
Demographic classification using image components
Pyush Agrawal,Rajeev Sharma +1 more
TL;DR: In this article, a system and method for automatically extracting the demographic information from images is presented, which detects the face in an image, locates different components, extracts component features, and then classifies the components to identify the age, gender, or ethnicity of the person(s) in the image.