A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finger tracking methods using eyesweb
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
A Camera-Based Music-Making Tool for Physical Rehabilitation
Computer Music Journal
Educational violin transcription by fusing multimedia streams
Proceedings of the international workshop on Educational multimedia and multimedia education
Visual analysis of fingering for pedagogical violin transcription
Proceedings of the 15th international conference on Multimedia
Vision-based guitarist fingering tracking using a Bayesian classifier and particle filters
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Analysis of erhu playing and design of learning environment for novice erhu player
ITHET'10 Proceedings of the 9th international conference on Information technology based higher education and training
guitAR: supporting guitar learning through mobile projection
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Using mobile projection to support guitar learning
SG'11 Proceedings of the 11th international conference on Smart graphics
Identifying attack articulations in classical guitar
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Automatic detection of musicians' ancillary gestures based on video analysis
Expert Systems with Applications: An International Journal
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This article presents a method to visually detect and recognize fingering gestures of the left hand of a guitarist. This method has been developed following preliminary manual and automated analysis of video recordings. These first analyses led to some important findings about the design methodology of a vision system for guitarist fingering, namely the focus on the effective gesture, the consideration of the action of each individual finger, and a recognition system not relying on comparison against a knowledge base of previously learned fingering positions. Motivated by these results, studies on three aspects of a complete fingering system were conducted: the first on finger tracking; the second on strings and frets detection; and the last one on movement segmentation. Finally, these concepts were integrated into a prototype and a system for left hand fingering detection was developed.