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Adaptive Segmentation for Air Gestures Identification

[+] Author Affiliations
Mostafa Ghobadi, Ehsan T. Esfahani

University at Buffalo, Buffalo, NY

Paper No. DETC2014-34809, pp. V01BT02A030; 5 pages
doi:10.1115/DETC2014-34809
From:
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1B: 34th Computers and Information in Engineering Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4629-2
  • Copyright © 2014 by ASME

abstract

Having a user-friendly Human-CAD interaction with high speed and accuracy plays a key role in development of future intelligent modeling environments. A major part of this puzzle is sketch identification using either 2D gestures — which is commonly recorded from mouse, light pen and touchpad — or air gestures captured from some newly emerged devices such as Leap Motion and Soft-Kinect. To this end, we present a leaning based technique for segmentation of air gestures. The proposed technique can detect the separation points of any single-stroke air gesture using specific motion features such as speed, curvature and center of curvature. Two types of separation points are considered: 1) rough separation points or simply corner points and 2) soft separation points such as inflection points. The segmentation is performed in two steps: Support Vector Machine (SVM) is used to adaptively differentiate the corner points from regular points. A soft segmentation method is then implemented to further break the rough segments into a set of smaller arcs and lines based on sudden change in the center of curvature. The experimental validation shows robust performance of the proposed method and low computation expenses.

Copyright © 2014 by ASME

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