An Accurate Hand Segmentation Approach using a Structure based Shape Localization Technique

Jose M. Saavedra, Benjamin Bustos, Violeta Chang

2013

Abstract

Hand segmentation is an important stage for a variety of applications such as gesture recognition and biometrics. The accuracy of the hand segmentation process becomes more critical in applications that are based on hand measurements as in the case of biometrics. In this paper, we present a very accurate hand segmentation technique, relying on both hand localization and color information. First, our proposal locates a hand on an input image, the hand location is then used to extract a training region which will play a critical role for segmenting the whole hand in an accurate way. We use a structure-based method (STELA), originally proposed for 3D model retrieval, for the hand localization stage. STELA exploits not only locality but also structural information of the hand image and does not require a large image collection for training. Second, our proposal separates the hand region from the background using the color information captured from the training region. In this way, the segmentation depends only on the user skin color. This segmentation approach allows us to handle a variety of skin colors and illumination conditions. In addition, our proposal is characterized by being fully automatic, where a user calibration stage is not required. Our results show a 100% in the hand localization process under different kinds of images and a very accurate hand segmentation achieving over 90% of correct segmentation at the expense of having only 5% for false positives.

References

  1. Canny, J. (1986). A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(6).
  2. Huang, D.-S., Jia, W., and Zhang, D. (2008). Palmprint verification based on principal lines. Pattern Recognition, 41.
  3. Jones, M. J. and Rehg, J. M. (2002). Statistical color models with application to skin detection. Int. Journal Comput. Vision, 46.
  4. Kovesi, P. D. (2000). MATLAB and Octave functions for computer vision and image processing. Available from: <http://www.csse.uwa.edu.au/ pk/research/ matlabfns/>.
  5. Lew, Y., Ramli, A., S.Y.Koay, Ali, R., and Prakash, V. (2002). A hand segmentation scheme using clustering technique in homogeneous background. In Proc. of 2nd Student Conference on Research and Development.
  6. Mikolajczyk, K. and Schmid, C. (2004). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 60(1):63-86.
  7. Saavedra, J. M., Bustos, B., Scherer, M., and Schreck, T. (2011). STELA: Sketch-based 3d model retrieval using a structure-based local approach. Submitted to ACM-ICMR.
  8. Soille, P. (1999). Morphological Image Analysis: Principles and Applications. Springer-Verlag Telos.
  9. Viola, P. and Jones, M. (2002). Robust real-time object detection. Int. Journal of Computer Vision.
  10. Wachs, J., Klsch, M., Stern, H., and Edan, Y. (2011). Vision-based hand gesture interfaces: Chall. and innov. Communications of the ACM.
  11. Yörük, E., Dutag?aci, H., and Sankur, B. (2006). Hand biometrics. Image and Vision Computing, 24:483-497.
  12. Yuan, M., Farbiz, F., Manders, C. M., and Tang, K. Y. (2008). Robust hand tracking using a simple color classification technique. In Proc. of The 7th ACM SIGGRAPH Int. Conf. on Virtual-Reality Continuum and Its Applications in Industry.
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Paper Citation


in Harvard Style

Saavedra J., Bustos B. and Chang V. (2013). An Accurate Hand Segmentation Approach using a Structure based Shape Localization Technique . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 321-326. DOI: 10.5220/0004288703210326


in Bibtex Style

@conference{visapp13,
author={Jose M. Saavedra and Benjamin Bustos and Violeta Chang},
title={An Accurate Hand Segmentation Approach using a Structure based Shape Localization Technique},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={321-326},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004288703210326},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - An Accurate Hand Segmentation Approach using a Structure based Shape Localization Technique
SN - 978-989-8565-47-1
AU - Saavedra J.
AU - Bustos B.
AU - Chang V.
PY - 2013
SP - 321
EP - 326
DO - 10.5220/0004288703210326