ACM SIGGRAPH ASIA 2008 educators programme
Fingertip detection with morphology and geometric calculation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Model-based hand tracking by chamfer distance and adaptive color learning using particle filter
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Seeing double: reconstructing obscured typed input from repeated compromising reflections
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
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This paper proposes a vision-based method for detecting the positions of fingertips of a hand playing a guitar. We detect the skin color of a guitar player's hand by using on-line adaptation of color probabilities and a Bayesian classifier which can cope with considerable illumination changes and a dynamic background. The results of hand segmentation are used to train an artificial neural network. A set of Gabor filters is utilized to compute a lower-dimensional representation of the image. Then an LLM (Local-Linear-Mapping)-network is applied to map and estimate fingertip positions smoothly. The system enables us to visually detect the fingertips even when the fingertips are in front of skin-colored surfaces and/or when the fingers are not fully stretched out. Representative experimental results are also presented.