Open AccessBook
Human Computer Interaction Using Hand Gestures
Prashan Premaratne
- 26 Mar 2014
56
TL;DR: In this article, the state-of-the-art hand gesture recognition approaches and how they evolved from their inception have been discussed and discussed for the past 8 years and how the future might turn out to be using HCI.
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Abstract: Human computer interaction (HCI) plays a vital role in bridging the 'Digital Divide', bringing people closer to consumer electronics control in the 'lounge'. Keyboards and mouse or remotes do alienate old and new generations alike from control interfaces. Hand Gesture Recognition systems bring hope of connecting people with machines in a natural way. This will lead to consumers being able to use their hands naturally to communicate with any electronic equipment in their 'lounge.' This monograph will include the state of the art hand gesture recognition approaches and how they evolved from their inception. The author would also detail his research in this area for the past 8 years and how the future might turn out to be using HCI. This monograph will serve as a valuable guide for researchers (who would endeavour into) in the world of HCI.
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Citations
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Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier - A Review
TL;DR: Evaluating the performance of the KNN using a large number of distance measures, tested on a number of real-world data sets, with and without adding different levels of noise found that a recently proposed nonconvex distance performed the best when applied on most data sets comparing with the other tested distances.
Depth-Based Hand Pose Estimation: Methods, Data, and Challenges
TL;DR: An extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame, defines a consistent evaluation criteria, rigorously motivated by human experiments and introduces a simple nearest-neighbor baseline that outperforms most existing systems.
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•Posted Content
Depth-based hand pose estimation: methods, data, and challenges
TL;DR: In this article, the authors provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame, and define a consistent evaluation criteria, rigorously motivated by human experiments.
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