Abstract
Motion estimation by means of spatio-temporal energy filters –velocity tuned filters– is known to be robust to noise and aliasing and to allow an easy treatment of the aperture problem. In this paper we propose a motion representation based on the composition of spatio-temporal energy features, i.e., responses of a set of filters in phase quadrature tuned to different scales and orientations. Complex motion patterns are identified by unsupervised cluster analysis of energy features. The integration criterion reflects the degree of alignment of maxima of the features’s amplitude, which is related to phase congruence. The composite-feature representation has been applied to motion segmentation with a geodesic active model both for initialization and image potential definition. We will show that the resulting method is able to handle typical problems, such as partial and total occlusions, large inter-frame displacements, moving background and noise.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sato, K., Aggarwal, J.: Temporal spatio-temporal transform and its application to tracking and interaction. Comput. Vis. Image Underst. 96, 100–128 (2000)
Boykov, Y., Huttenlocher, D.: Adaptive bayesian recognition in tracking rigid objects. In: IEEE CVPR, vol. 2, pp. 697–704 (2000)
Nguyen, H., Smeulders, A.: Fast occluded object tracking by a robust appearance filter. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1099–1104 (2004)
Montoliu, R., Pla, F.: An iterative region-growing algorithm for motion segmentation and estimation. Int. J. Intell. Syst. 20, 577–590 (2005)
Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 22, 266–279 (2000)
Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. Int. J. Comput. Vis. 12, 43–77 (1994)
Heeger, D.: Model for the extraction of image flow. J. Opt. Soc. Am. A 4, 1471–1555 (1987)
Simoncelli, E., Adelson, E.: Computing optical flow distributions using spatio-temporal filters. Technical Report 165, MIT Media Lab. Vision and Modeling, Massachusetts (1991)
Watson, A., Ahumada, A.: Model for human visual-motion sensing. J. Opt. Soc. Am. A 2, 322–342 (1985)
Adelson, E., Bergen, J.: Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299 (1985)
Fleet, D.: Measurement of Image Velocity. Kluwer Academic Publishers, Massachusetts (1992)
Chamorro-Martínez, J., Fdez-Valdivia, J., García, J., Martínez-Baena, J.: A frequency domain approach for the extraction of motion patterns. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing, vol. 3, pp. 165–168 (2003)
Morrone, M., Owens, R.: Feature detection from local energy. Pattern Recognit. Lett. 6, 303–313 (1987)
Venkatesh, S., Owens, R.: On the classification of image features. Pattern Recognit. Lett. 11, 339–349 (1990)
Weickert, J., Kühne, G.: Fast methods for implicit active contour models. In: Osher, S., Paragios, N. (eds.) Geometric Level Set Methods in Imaging, Vision and Graphics, pp. 43–58. Springer, New York (2003)
Tsechpenakis, G., Rapantzikos, K., Tsapatsoulis, N., Kollias, S.: A snake model for object tracking in natural sequences. Signal Process. Image Commun. 19, 219–238 (2004)
Dosil, R., Pardo, X., Fdez-Vidal, X.: Data driven detection of composite feature detectors for 3D image analysis. Image Vis. Comput. 32, 225–238 (2006)
Field, D.: What is the goal of sensory coding. Neural Comput. 6, 559–601 (1994)
Faas, F., van Vliet, L.: 3D-orientation space; filters and sampling. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 36–42. Springer, Heidelberg (2003)
Kovesi, P.: Invariant Measures of Image Features from Phase Information. PhD thesis, The University or Western Australia (1996)
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
Pal, N., Biswas, J.: Cluster validation using graph theoretic concepts. Pattern Recognit. 30, 847–857 (1996)
Dosil, R., Pardo, X.: Generalized ellipsoids and anisotropic filtering for segmentation improvement in 3D medical imaging. Image Vis. Comput. 21, 325–343 (2003)
URL: http://www-gva.dec.usc.es/~rdosil/motion_segmentation_examples.htm (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dosil, R., Pardo, X.M., Fdez-Vidal, X.R., García, A. (2006). Spatio-temporal Composite-Features for Motion Analysis and Segmentation. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_30
Download citation
DOI: https://doi.org/10.1007/11864349_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
eBook Packages: Computer ScienceComputer Science (R0)