63 research outputs found
Supplemental material for this article is available online.
No full textSupplemental Material for SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation by Lauren Zimmermann, Subarna Bhattacharya, Soumik Purkayastha, Ritoban Kundu, Ritwik Bhaduri, Parikshit Ghosh and Bhramar Mukherjee, in Studies in Microeconomics</p
Real-time hand gesture recognition for uncontrolled environments using adaptive SURF tracking and hidden conditional random fields
No full textChallenges from the uncontrolled environments are the main difficulties in making hand gesture recognition methods robust in real-world scenarios. In this paper, we propose a real-time and purely vision-based method for hand gesture recognition in uncontrolled environments. A novel tracking method is introduced to track multiple hand candidates from the first frame. The movement directions of all hand candidates are extracted as trajectory features. A modified HCRF model is used to classify gestures. The proposed method can survive challenges including: gesturing hand out of the scene, pause during gestures, complex background, skin-coloured regions moving in background, performers wearing short sleeve and face overlapping with hand. The method has been tested on Palm Graffiti Digits database and Warwick Hand Gesture database. Experimental results show that the proposed method can perform well in uncontrolled environments
A framework for real-time hand gesture recognition in uncontrolled environments with partition matrix model based on hidden conditional random fields
No full textThe main obstructions of making hand gesture recognition methods robust in real-world applications are the challenges from the uncontrolled environments, including: gesturing hand out of the scene, pause during gestures, complex background, skin-coloured regions moving in background, performers wearing short sleeve and face overlapping with hand. Therefore, a framework for real-time hand gesture recognition in uncontrolled environments is proposed in this paper. A novel tracking scheme is proposed to track multiple hand candidates in unconstrained background, and a weighting model for gesture classification based on Hidden Conditional Random Fields which takes trajectories of multiple hand candidates under different frame rates into consideration is also introduced. The framework achieved invariance under change of scale, speed and location of the hand gestures. The Experimental results of the proposed framework on Palm Graffiti Digits database and Warwick Hand Gesture database show that it can perform well in uncontrolled environments
Critical behavior near the ferromagnetic – paramagnetic phase transition in La0.7Sr0.3MnO3+d nanowires synthesized by hydrothermal method
No full textDesign of an Unlimited Range Web Browser Controlled Robot with Self-Adapting Fuzzy Logic Controller
No full textExploring the utility of axial lumbar MRI for automatic diagnosis of intervertebral disc abnormalities
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Performance of a silicon photovoltaic module under enhanced illumination and selective filtration of incoming radiation with simultaneous cooling
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