Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Hand Gesture Recognition Using Fast Multi-scale Analysis
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Tracking HoG Descriptors for Gesture Recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
ActiveTheatre: a collaborative, event-based capture and access system for the operating theatre
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Preservation of asepsis in operating rooms is essential for limiting the contamination of patients by hospital-acquired infections. Strict rules hinder surgeons from interacting directly with any sterile equipement, requiring the intermediary of an assistant or a nurse. Such indirect control may prove itself clumsy and slow up the performed surgery. Gesture-based Human-Computer Interfaces show a promising alternative to assistants and could help surgeons in taking direct control over sterile equipements in the future without jeopardizing asepsis. This paper presents the experiments we led on hand posture feature selection and the obtained results. State-of-the-art description methods classified in four different categories (i.e. local, semi-local, global and geometric description approaches) have been selected to this end. Their recognition rates when combined with a linear Support Vector Machine classifier are compared while attempting to recognize hand postures issued from an ad-hoc database. For each descriptor, we study the effects of removing the background to simulate a segmentation step and the importance of a correct hand framing in the picture. Obtained results show all descriptors benefit to various extents from the segmentation step. Geometric approaches perform best, followed closely by Dalal et al.'s Histogram of Oriented Gradients.