
Proceedings Paper
Finger tracking for hand-held device interface using profile-matching stereo visionFormat | Member Price | Non-Member Price |
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Paper Abstract
Hundreds of millions of people use hand-held devices frequently and control them by touching the screen with their
fingers. If this method of operation is being used by people who are driving, the probability of deaths and accidents
occurring substantially increases. With a non-contact control interface, people do not need to touch the screen. As a
result, people will not need to pay as much attention to their phones and thus drive more safely than they would
otherwise. This interface can be achieved with real-time stereovision. A novel Intensity Profile Shape-Matching
Algorithm is able to obtain 3-D information from a pair of stereo images in real time. While this algorithm does have a
trade-off between accuracy and processing speed, the result of this algorithm proves the accuracy is sufficient for the
practical use of recognizing human poses and finger movement tracking. By choosing an interval of disparity, an object
at a certain distance range can be segmented. In other words, we detect the object by its distance to the cameras. The
advantage of this profile shape-matching algorithm is that detection of correspondences relies on the shape of profile and
not on intensity values, which are subjected to lighting variations. Based on the resulting 3-D information, the
movement of fingers in space from a specific distance can be determined. Finger location and movement can then be
analyzed for non-contact control of hand-held devices.
Paper Details
Date Published: 4 February 2013
PDF: 9 pages
Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, 86620H (4 February 2013); doi: 10.1117/12.2013702
Published in SPIE Proceedings Vol. 8662:
Intelligent Robots and Computer Vision XXX: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)
PDF: 9 pages
Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, 86620H (4 February 2013); doi: 10.1117/12.2013702
Show Author Affiliations
Yung-Ping Chang, Brigham Young Univ. (United States)
Dah-Jye Lee, Brigham Young Univ. (United States)
Jason Moore, Brigham Young Univ. (United States)
Dah-Jye Lee, Brigham Young Univ. (United States)
Jason Moore, Brigham Young Univ. (United States)
Published in SPIE Proceedings Vol. 8662:
Intelligent Robots and Computer Vision XXX: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)
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