Book ChapterDOI
Gesture Modelling and Recognition by Integrating Declarative Models and Pattern Recognition Algorithms
Alessandro Carcangiu,Lucio Davide Spano,Giorgio Fumera,Fabio Roli +3 more
- Vol. 10484, pp 84-95
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TLDR
DEICTIC is introduced, a compositional and declarative gesture description model which uses basic Hidden Markov Models (HMMs) to recognize meaningful pre-defined primitives (gesture sub-parts), and uses a composition of basic HMMs to recognize complex gestures.Abstract:
Gesture recognition approaches based on computer vision and machine learning mainly focus on recognition accuracy and robustness. Research on user interface development focuses instead on the orthogonal problem of providing guidance for performing and discovering interactive gestures, through compositional approaches that provide information on gesture sub-parts. We make a first step toward combining the advantages of both approaches. We introduce DEICTIC, a compositional and declarative gesture description model which uses basic Hidden Markov Models (HMMs) to recognize meaningful pre-defined primitives (gesture sub-parts), and uses a composition of basic HMMs to recognize complex gestures. Preliminary empirical results show that DEICTIC exhibits a similar recognition performance as “monolithic” HMMs used in state-of-the-art vision-based approaches, retaining at the same time the advantages of declarative approaches.read more
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Journal ArticleDOI
DEICTIC: A compositional and declarative gesture description based on hidden markov models
TL;DR: DEICTIC, a compositional and declarative description for stroke gestures, which uses basic Hidden Markov Models to recognise meaningful predefined primitives (gesture sub-parts) and it composes them to recognise complex gestures and reaches an accuracy comparable with state-of-the-art approaches.
Journal ArticleDOI
DG3: Exploiting Gesture Declarative Models for Sample Generation and Online Recognition
Stefano Dessì,Lucio Davide Spano +1 more
TL;DR: DG3, an end-to-end method for exploiting gesture interaction in user interfaces, is introduced and it is shown that the method outperforms existing approaches for online recognition and has comparable accuracy with offline methods after a few gesture segments.
A Declarative and Classifier Gesture Recognition Method for Creating an Effective Feedback and Feedforward System.
TL;DR: The main goal of this Ph.D. is finding a way for filling the gap between Machine Learning and declarative and compositional approaches, bridging the gap in recognition of gestures in an interactive application.
Proceedings ArticleDOI
Integrating declarative models and HMMs for online gesture recognition
TL;DR: A work in progress research for connecting the algorithm used for accurately recognizing the user movements and the guidance provided to users while executing gestures and increasing their effectiveness is discussed.
References
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Journal ArticleDOI
Natural user interfaces are not natural
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