A graph modeling strategy for multi-touch gesture recognition
Zhaoxin Chen,Eric Anquetil,Harold Mouchère,Christian Viard-Gaudin +3 more
- pp 259-264
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TLDR
This research points out a possibility of integrating together raw ink, direct manipulation and indirect command in many gesture-based complex application such as a sketch drawing application.Abstract:
In most applications of touch based humancomputer interaction, multi-touch gestures are used for directlymanipulating the interface such as scaling, panning, etc. In thispaper, we propose using multi-touch gesture as indirectcommand, such as redo, undo, erase, etc., for the operatingsystem. The proposed recognition system is guided by temporal,spatial and shape information. This is achieved using a graphembedding approach where all previous information are used.We evaluated our multi-touch recognition system on a set of 18different multi-touch gestures. With this graph embeddingmethod and a SVM classifier, we achieve 94.50% recognition rate.We believe that our research points out a possibility ofintegrating together raw ink, direct manipulation and indirectcommand in many gesture-based complex application such as asketch drawing application.read more
Citations
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Proceedings ArticleDOI
Learning to Recognize Touch Gestures: Recurrent vs. Convolutional Features and Dynamic Sampling
TL;DR: A fully automatic method for learning gestures on big touch devices in a potentially multi-user context based on deep neural networks, which features a novel dynamic sampling and temporal normalization component, transforming variable length gestures into fixed length representations while preserving finger/surface contact transitions.
Recognize multi-touch gestures by graph modeling and matching
TL;DR: Results prove that the new generic graph model presented can be fruitfully used for multi-touch gesture pattern recognition purpose with the classifier of graph embedding and SVM.
Book ChapterDOI
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
TL;DR: In this paper, a VAE-based interaction protocol is proposed to map a 2D user input to 3D actions in virtual environments using reinforcement learning (RL) for navigating in virtual 3D environments.
Posted Content
Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling
TL;DR: In this article, the authors propose a fully automatic method for learning gestures on big touch devices in a potentially multi-user context, based on deep neural networks, which features a dynamic sampling and temporal normalization component, transforming variable length gestures into fixed length representations while preserving finger/surface contact transitions, that is, the topology of the signal.
Dissertation
Recognition and interpretation of multi-touch gesture interaction
TL;DR: A reject option based multi-classifier early recognition system to recognize multi-touch gestures in early stage and a graph modeling to characterize these motion features and develop a graph based analysis and recognition system are proposed.
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