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
Interactive evaluation of assembly sequences using augmented reality
TL;DR: An interactive tool for evaluating assembly sequences using the novel human-computer interface of augmented reality to be able to consider various sequencing alternatives of the manufacturing design process by manipulating both virtual and real prototype components.
Journal ArticleDOI
Enabling collaborative geoinformation access and decision‐making through a natural, multimodal interface
Alan M. MacEachren,Guoray Cai,Rajeev Sharma,Ingmar Rauschert,Isaac Brewer,Levent Bolelli,B. Shaparenko,Sven Fuhrmann,Hongmei Wang +8 more
TL;DR: An approach for designing natural, multimodal, multiuser dialogue‐enabled interfaces to geographic information systems that make use of large‐screen displays and integrated speech–gesture interaction is developed.
Proceedings ArticleDOI
From facial expression to level of interest: a spatio-temporal approach
TL;DR: This paper presents a novel approach to recognize the six universal facial expressions from visual data and use them to derive the level of interest using psychological evidences using a two-step classification built on the top of refined optical flow computed from sequence of images.
Proceedings ArticleDOI
A real-time framework for natural multimodal interaction with large screen displays
TL;DR: This paper presents a framework for designing a natural multimodal human computer interaction (HCI) system and found that the system performed according to its specifications in 95% of the cases and that users showed ad-hoc proficiency, indicating natural acceptance of such systems.
Patent
Multi-modal gender classification using support vector machines (SVMs)
TL;DR: In this article, a multi-modal system for determining the gender of a person using support vector machines (SVMs) was proposed. But the gender classification was first performed on visual (thumbnail frontal face) and audio (feature extracted from speech) data using SVM.