![](bg4.png)
found that using the mean value gives visually more
pleasing and consistent results. A quantitative study
is necessary to confirm this observation.
6 CONCLUSIONS
We have presented a novel region-growingmethod for
hand segmentation. The main difference to previous
methods is the use of a perception-based colour space
and a classification function using edge detection in-
formation in combination with mean colour values
rather than neighborhood information.
We have compared our method with traditional
skin classifiers and demonstrated that it is superior.
While the comparison has to be treated with caution
due to the lack of edge information when using the
skin classifiers, we believethat the results still demon-
strate the usefulness of our method as a starting point
for hand tracking applications.
We evaluated our method for different illumina-
tion conditions, backgrounds and skin colours, and
found that it is sufficiently stable and forms a suitable
foundation for low-cost hand tracking applications.
We have started to experiment with active contour
models, but found less improvement than expected
due to the high curvature of the hand silhouette.
7 FUTURE WORK
The next steps for our application are to estimate 3D
motion of the hand by using a 3D hand model with
kinematic constraints. In order to resolve ambigui-
ties in the mapping process we plan to use simplified
markers, e.g., stickers, coloured rubber bands or lip-
stick marks. This type of markers gives less reliable
results than traditional markers and we can not expect
users to place them correctly - however, such markers
are easy to use, cost effective, do not constrain mo-
bility, and can be used even if the patient suffers from
conditions such as swellings and sores on the hand.
REFERENCES
Chen, Q., Georganas, N., and Petriu, E. (2007). Real-time
vision-based hand gesture recognition using haar-like
features. In Instrumentation and Measurement Tech-
nology Conference Proceedings, 2007. IMTC 2007.
IEEE, pages 1–6.
Chong, H. Y., Gortler, S. J., and Zickler, T. (2008).
A perception-based color space for illumination-
invariant image processing. ACM Trans. Graph.,
27(3):1–7.
Hsu, R.-L., Abdel-Mottaleb, M., and Jain, A. K. (2002).
Face detection in color images. IEEE Transactions on
Pattern Analysis and Machine Intelligence, 24:696–
706.
Kakumanu, P., Makrogiannis, S., and Bourbakis, N. (2007).
A survey of skin-color modeling and detection meth-
ods. Pattern Recogn., 40(3):1106–1122.
Kovac, J., Peer, P., and Solina, F. (2003). Human skin color
clustering for face detection. In EUROCON 2003.
Computer as a Tool., volume 2, pages 144–148.
Mahmoudi, F. and Parviz, M. (2006). Visual hand tracking
algorithms. In GMAI, pages 228–232.
Stauffer, C. and Grimson, W. (1999). Adaptive background
mixture models for real-time tracking. In IEEE Com-
puter Society Conference on Computer Vision and
Pattern Recognition, volume 2, page 252.
Stenger, B., Mendona, P. R. S., and Cipolla, R. (2001).
Model-based 3d tracking of an articulated hand. Com-
puter Society Conference on Computer Vision and
Pattern Recognition, 2:310.
Stenger, B., Thayananthan, A., Torr, P. H. S., and Cipolla,
R. (2006). Model-based hand tracking using a hierar-
chical bayesian filter. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 28(9):1372–1384.
UK Department of Health (2009). UK telecare policy and
strategy. http://www.pasa.nhs.uk/PASAWeb/
Productsandservices/Telecare/
Governmentpriorities.htm.
Vassili, V. V., Sazonov, V., and Andreeva, A. (2003). A
survey on pixel-based skin color detection techniques.
In Proc. Graphicon-2003, pages 85–92.
Wang, R. Y. and Popovi´c, J. (2009). Real-time hand-
tracking with a color glove. In SIGGRAPH ’09: ACM
SIGGRAPH 2009 papers, pages 1–8, New York, NY,
USA. ACM.
Wessel, J. (2004). The effectiveness of hand exercises for
persons with rheumatoid arthritis: A systematic re-
view. Journal of Hand Therapy, 17(2):174–180.
Yang, J., Lu, W., and Waibel, A. (1998). Skin-color model-
ing and adaptation. In ACCV ’98: Proceedings of the
Third Asian Conference on Computer Vision-Volume
II, pages 687–694, London, UK. Springer-Verlag.
VISAPP 2010 - International Conference on Computer Vision Theory and Applications
266