Finger tracking methods using eyesweb

Authors:
Anne-Marie Burns;Barbara Mazzarino
Affiliations:
Input Devices and Music Interaction Lab, Schulich School of Music, McGill University, Montréal, Québec, Canada;InfoMus Lab, DIST – University of Genova, Genova, Italy
Venue:
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Year:
2005

Citing 5
Cited 1

A survey of the Hough transform

Computer Vision, Graphics, and Image Processing
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures

Communications of the ACM
Integrating paper and digital information on EnhancedDesk: a method for realtime finger tracking on an augmented desk system

ACM Transactions on Computer-Human Interaction (TOCHI)
Visual tracking of bare fingers for interactive surfaces

Proceedings of the 17th annual ACM symposium on User interface software and technology

Visual methods for the retrieval of guitarist fingering

NIME '06 Proceedings of the 2006 conference on New interfaces for musical expression

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper compares different algorithms for tracking the position of fingers in a two-dimensional environment. Four algorithms have been implemented in EyesWeb, developed by DIST-InfoMus laboratory. The three first algorithms use projection signatures, the circular Hough transform, and geometric properties, and rely only on hand characteristics to locate the finger. The fourth algorithm uses color markers and is employed as a reference system for the other three. All the algorithms have been evaluated using two-dimensional video images of a hand performing different finger movements on a flat surface. Results about the accuracy, precision, latency and computer resource usage of the different algorithms are provided. Applications of this research include human-computer interaction systems based on hand gesture, sign language recognition, hand posture recognition, and gestural control of music.