SMASH: a distributed sensing and processing garment for the classification of upper body postures
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Real-Time Image-Based Motion Detection Using Color and Structure
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Automatic recognition of finger spelling for LIBRAS based on a two-layer architecture
Proceedings of the 2010 ACM Symposium on Applied Computing
Gesture recognition under small sample size
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A person independent system for recognition of hand postures used in sign language
Pattern Recognition Letters
Robust hand posture recognition integrating multi-cue hand tracking
Edutainment'10 Proceedings of the Entertainment for education, and 5th international conference on E-learning and games
Online multiple tasks one-shot learning of object categories and vision
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Real time hand gesture recognition including hand segmentation and tracking
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A case study of user immersion-based systematic design for serious heritage games
Multimedia Tools and Applications
Fusing multi-modal features for gesture recognition
Proceedings of the 15th ACM on International conference on multimodal interaction
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Developing new techniques for human-computer interaction is very challenging. Vision-based techniques have the advantage of being unobtrusive and hands are a natural device that can be used for more intuitive interfaces. But in order to use hands for interaction, it is necessary to be able to recognize them in images. In this paper, we propose to apply to the hand posture classification and recognition tasks an approach that has been successfully used for face detection [3]. The features are based on the Modified Census Transform and are illumination invariant. For the classification and recognition processes, a simple linear classifier is trained, using a set of feature lookup-tables. The database used for the experiments is a benchmark database in the field of posture recognition. Two protocols have been defined. We provide results following these two protocols for both the classification and recognition tasks. Results are very encouraging.