Abstract
Motion detection plays a central role in several visual environments: knowledge of object velocities and trajectories is fundamental in scene interpretation and segmentation. This task appears a simple problem, but detecting moving objects is very difficult, in fact this is a problem that cannot be considered completely solved today [1] [2] [3].
In this paper we present a novel method that uses two different approaches: a “neural” one and an algorithmic one. In fact, a Multilayer Perceptron is used in the first stage, in order to detect some motion areas in the scene [5] [6]; a matching algorithm is then used to obtain a sparse optical flow and to compute the epipolar geometry of the moving camera [7] [8]; and, finally, a refinement algorithm is used to produce a denser optical flow field. Thus this method can extract features automatically from moving objects in a scene discarding stationary ones. This approach seems to be very useful for tracking and motion segmentation.
This work was developed in the context of JACOB project, to achieve the automatic retrieval of images based on motion [9].
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
S. Negahdaripour, S. Lee, Motion recovery from image sequences using only first order optical flow information, International Journal of computer vision, 9:3, 163–184 (1992)
A. Singh, P. Allen, Image-Flow Computation An estimation-theoretic framework and an unified perspective, CVGIP-IU, vol. 56, no. 2, September 1992
J. Weber, J. Malik, Robust computation of optical flow in a multi-scale differential framework, IEEE Fourth International Conference on Computer Vision 2/93
M.J.D. Powel, Restart Procedures for the Conjugate Gradient Method, Mathematical Programming, Vol. 12, pp. 241–254
A. Abruzzo, G.A.M. Gioiello, M. La Cascia, F. Sorbello, Motion Detection From Image Sequences Using A New Fully Digital VLSI Neural Architecture, EUFIT95 - August 26–31, 1995, Aachen, Germany
A. Abruzzo, G.A.M. Gioiello, M. La Cascia, F. Sorbello, A New Fully Digital Neural Architecture For Motion Detection From Image Sequences, EANN95 - International Conference on Engineering Applications of Neural Networks, August 21–23, 1995, Helsinki, Finland
Q.T. Luong, R. Deriche, O.D. Faugeras, T. Papadopoulo, On Determining the Fundamental Matrix, Technical Report 1894, INULA, Sophia-Antipolis, France, 1993
Z. Zhang, R. Deriche, O. Faugeras, Q.-T. Luong, A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry, Artificial Intelligence Journal, Vol.78, pages 87–119, October 1995. Also Research Report No.2273, INRIA Sophia-Antipolis
M. La Cascia, E. Ardizzone, JACOB: Just a content-based query system for video databases, IEEE International Conference On ACOUSTICS, SPEECH AND SIGNAL PROCESSING, May 7–10, 1996, Atlanta, Georgia (USA)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer-Verlag London Limited
About this paper
Cite this paper
Criminisi, A., Gioiello, G.A.M., Molinelli, D., Sorbello, F. (1997). An Integrated Neural and Algorithmic System for Optical Flow Computation. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN VIETRI-96. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0951-8_35
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0951-8_35
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1240-2
Online ISBN: 978-1-4471-0951-8
eBook Packages: Springer Book Archive