Abstract
The paper reports about a performance comparison within a joint project of omputer vision, and sport and exercise sciences. The project is directed on the understanding of human motion based on shape features and kinetic studies. Three shape recovery techniques, a traditional technique as used in sport and exercise sciences (manual measurement based on an elliptical zone assumption) and two omputer vision techniques (based on a small number of of luding ontours, and a new ombination of photometri stereo and shape from boundaries), are ompared using a mannequin as test object. The omputer vision techniques have been designed to go towards dynamic shape recovery (humans in motion). The paper reports about these three techniques and their measurement accuracies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
N. Austin: Estimation of segmental inertial parameter variation in hildren: Implication for gait analysis. Master thesis, Sport and Exercise Sciences, The University of Au kland (2000).
R.F. Chandler, C.E. Clauster, J.T. McConville, H.M. Reynolds, J.W. Young: Investigation of inertial properties of the human body. AMRL Technical Report, TR–74–137 (1975). 43
C.E. Clauster, J.T. McConville, J.W. Young: Weight,volume and enter of mass of segments of the human body. AMRL Technical Report, TR–69–70 (1969). 43
W.D. Dempster: Space requirements of the seated operator.WADC Te hnical Report 55–159 (1955).
R. Frankot, R. Chellappa: Amethod for enforcing integrability in shape from shading algorithms. IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-10 (1988) 439–451.47
R.K. Jensen: Estimation of the biome hani al properties of three body types using a photogrammetric method. J. of Biomechanics 11 (1978) 349–358. 45, 46
R. Klette, K.Shlüns, Height data from gradient fields Proc.SPIE, 2908, pp.204–215, 1996.47
R. Klette, K.S hlüns, A. Koschan: Computer Vision: Three-dimensional Data from Images Springer, Singapore (1998).
J.J. Koenderink: Solid Shape Cambridge, MA, MIT Press (1990). 47
R.N. Marshall, R.K. Jensen, G.A. Wood: A general Newtonian simulation of an n-segment open hain model. J. of Biomechanics 18 (1985) 359–368. 43
B.M. Nigg, W. Herzog: Biomechanics of the Musculo-Skeletal System Wiley, New York (1994). 44
S. Tokai, T. Wada, T. Matsuyama: Real time 3D shape re onstruction using PC cluster system. Pro. 3rd Internat. Workshop Cooperative Distributed Vision Kyoto (1999) 171–187. 45
R.Y. Tsai: An efficient and accurate camera calibration te hnique for 3D ma hine vision. Proc. Internat. Conf. Computer Vision and Pattern Recognition (1986) 364–374. 47
R.J. Woodham: Photometric method for determining surface orientation from multiple images.Optical Engineering 19 (1980) 139–144.47
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Austin, N., Chen, Y., Klette, R., Marshall, R., Tsai, Ys., Zhang, Y. (2001). A Comparison of Feature Measurements for Kinetic Studies on Human Bodies. In: Klette, R., Peleg, S., Sommer, G. (eds) Robot Vision. RobVis 2001. Lecture Notes in Computer Science, vol 1998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44690-7_6
Download citation
DOI: https://doi.org/10.1007/3-540-44690-7_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41694-4
Online ISBN: 978-3-540-44690-3
eBook Packages: Springer Book Archive