The Community for Technology Leaders
RSS Icon
Issue No.09 - September (2010 vol.32)
pp: 1646-1658
Tobias Schuchert , Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe
Til Aach , RWTH Aachen University, Aachen
Hanno Scharr , Forschungszentrum Jülich, Jülich
We extend estimation of range flow to handle brightness changes in image data caused by inhomogeneous illumination. Standard range flow computes 3D velocity fields using both range and intensity image sequences. Toward this end, range flow estimation combines a depth change model with a brightness constancy model. However, local brightness is generally not preserved when object surfaces rotate relative to the camera or the light sources, or when surfaces move in inhomogeneous illumination. We describe and investigate different approaches to handle such brightness changes. A straightforward approach is to prefilter the intensity data such that brightness changes are suppressed, for instance, by a highpass or a homomorphic filter. Such prefiltering may, though, reduce the signal-to-noise ratio. An alternative novel approach is to replace the brightness constancy model by 1) a gradient constancy model, or 2) by a combination of gradient and brightness constancy constraints used earlier successfully for optical flow, or 3) by a physics-based brightness change model. In performance tests, the standard version and the novel versions of range flow estimation are investigated using prefiltered or nonprefiltered synthetic data with available ground truth. Furthermore, the influences of additive Gaussian noise and simulated shot noise are investigated. Finally, we compare all range flow estimators on real data.
Range flow, illumination changes, brightness constancy constraint, prefiltering, homomorphic filter, gradient constancy, structure tensor, 3D motion estimation.
Tobias Schuchert, Til Aach, Hanno Scharr, "Range Flow in Varying Illumination: Algorithms and Comparisons", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 9, pp. 1646-1658, September 2010, doi:10.1109/TPAMI.2009.162
[1] H. Spies, B. Jähne, and J. Barron, "Range Flow Estimation," Computer Vision and Image Understanding, vol. 85, no. 3, pp. 209-231, Mar. 2002.
[2] M. Yamamoto, P. Boulanger, J.A. Beraldin, and M. Rioux, "Direct Estimation of Range Flow on Deformable Shape from a Video Rate Range Camera," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 1, pp. 82-89, Jan. 1993.
[3] H. Gharavi and S. Gao, "3d Motion Estimation Using Range Data," IEEE Trans. Intelligent Transportation Systems, vol. 8, no. 1, pp. 133-143, Mar. 2007.
[4] H. Spies, H. Haußecker, B. Jähne, and J. Barron, "Differential Range Flow Estimation," Proc. DAGM Symp., pp. 309-316, 1999.
[5] Y. Zhang and C. Kambhamettu, "Integrated 3D Scene Flow and Structure Recovery from Multiview Image Sequences," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 2674-2681, 2000.
[6] S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, "Three-Dimensional Scene Flow," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 475-480, Mar. 2005.
[7] H. Scharr, "Towards a Multi-Camera Generalization of Brightness Constancy," Proc. Int'l Workshop Complex Motion, 2005.
[8] J. Barron, D. Fleet, and S. Beauchemin, "Performance of Optical Flow Techniques," Int'l J. Computer Vision, vol. 12, no. 1, pp. 43-77, 1994.
[9] B. Jähne, H. Haußecker, and P. Geissler, Handbook of Computer Vision and Applications, first ed. Academic Press, 1999.
[10] A. Bruhn, "Variational Optic Flow Computation, Accurate Modelling and Efficient Numerics," PhD thesis, Dept. of Math. and Computer Science, Saarland Univ., 2006.
[11] N. Papenberg, A. Bruhn, T. Brox, S. Didas, and J. Weickert, "Highly Accurate Optic Flow Computation with Theoretically Justified Warping," Int'l J. Computer Vision, vol. 67, no. 2, pp. 141-158, 2006.
[12] T.S.J. Denney and J.L. Prince, "Optimal Brightness Functions for Optical Flow Estimation of Deformable Motion," IEEE Trans. Image Processing, vol. 3, no. 2, pp. 178-191, Mar. 1994.
[13] H. Haußecker and D.J. Fleet, "Computing Optical Flow with Physical Models of Brightness Variation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 661-673, June 2001.
[14] T. Schuchert and H. Scharr, "Simultaneous Estimation of Surface Motion, Depth and Slopes Under Changing Illumination," Proc. DAGM Symp., pp. 184-193, 2007.
[15] D. Toth, T. Aach, and V. Metzler, "Illumination Invariant Change Detection," Proc. IEEE Southwest Symp. Image Analysis and Interpretation, pp. 3-7, 2000.
[16] A. Oppenheim, R. Schafer, and T.G. Stockham,Jr., "Nonlinear Filtering of Multiplied and Convolved Signals," Proc. IEEE, vol. 56, no. 8, pp. 1264-1291, Aug. 1968.
[17] J.-P. Pons, R. Keriven, and O. Faugeras, "Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score," Int'l J. Computer Vision, vol. 72, no. 2, pp. 179-193, 2007.
[18] F. Huguet and F. Devernay, "A Variational Method for Scene Flow Estimation from Stereo Sequences," Proc. IEEE Int'l Conf. Computer Vision, 2007.
[19] R. Li and S. Sclaroff, "Multi-Scale 3D Scene Flow from Binocular Stereo Sequences," Proc. IEEE Workshop Computer Vision, vol. 2, pp. 147-153, 2005.
[20] B. Lucas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," Proc. Defense Advanced Research Projets Agency Image Understanding Workshop, pp. 121-130, 1981.
[21] J. Bigün, G. Granlund, and J. Wiklund, "Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 775-790, Aug. 1991.
[22] A. Bruhn, J. Weickert, and C. Schnörr, "Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods," Int'l J. Computer Vision, vol. 61, no. 3, pp. 211-231, 2005.
[23] M.J. Black and P. Anandan, "The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields," Computer Vision and Image Understanding, vol. 63, no. 1, pp. 75-104, 1996.
[24] S.X. Ju, M.J. Black, and A.D. Jepson, "Skin and Bones: Multi-Layer, Locally Affine, Optical Flow and Regularization with Transparency," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 307-314, June 1996.
[25] T. Schuchert, T. Aach, and H. Scharr, "Range Flow for Varying Illumination," Proc. European Conf. Computer Vision, pp. 509-522, 2008.
[26] B. Jähne, Digitale Bildverarbeitung, fourth ed. Springer-Verlag, 1997.
[27] H. Arsenault and M. Denis, "Image Processing in Signal-Dependent Noise," Canadian J. Physics, vol. 61, pp. 309-317, 1983.
[28] H. Scharr and T. Schuchert, "Simultaneous Motion, Depth and Slope Estimation with a Camera-Grid," Proc. Vision, Modeling, and Visualization '06, pp. 81-88, 2006.
[29] H. Scharr, "Optimal Filters for Extended Optical Flow," Proc. Int'l Workshop Complex Motion, pp. 14-29, 2004.
[30] H. Spies, B. Jähne, and J.L. Barron, Surface Expansion from Range Data Sequences, pp. 163-169. Springer-Verlag, 2001.
[31] O. Nestares, D.J. Fleet, and D.J. Heeger, "Likelihood Functions and Confidence Bounds for Total Least Squares Estimation," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
15 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool