Abstract
In this paper, we propose a novel technique to address motion estimation and tracking. Such technique represents the motion field using a regular grid of thin-plate splines, and the moving objects using an implicit function on the image plane that is a cubic interpolation of a ”level set function” defined on this grid. Optical flow is determined through the deformation of the grid and consequently of the underlying image structures towards satisfying the constant brightness constraint. Tracking is performed in similar fashion through the consistent recovery in the temporal domain of the zero iso-surfaces of a level set that is the projection of the Free Form Deformation (FFD) implicit function according to the cubic spline formulation. Such an approach is a compromise between dense motion estimation and parametric motion models, introduces smoothness in an implicit fashion, is intrinsic, and can cope with important object deformations. Promising results demonstrate the potentials of our approach.
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References
Barron, J., Fleet, D., Beauchemin, S.: Performance of Optical Flow Techniques. International Journal of Computer Vision 12, 43–77 (1994)
Horn, B., Schunck, B.: Determinating Optical Flow. Artificial Intelligence 17, 185–203 (1981)
Weickert, J., Schnorr, C.: Variational optic flow computation with a spatio-temporal smoothness constraint. Journal of Mathematical Imaging and Vision 14, 245–255 (2001)
Lucas, B., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Huber, P.: Robust Statistics. John Wiley & Sons, Chichester (1981)
Black, M., Jepson, A.: Estimating optical flow in segmented images using variable-order parametric models with local deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 973–986 (1996)
Odobez, J.-M., Bouthemy, P.: Robust multiresolution estimation of parametric motion models. Journal of Visual Communication and Image Representation 6, 348–365 (1995)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. In: IEEE International Conference in Computer Vision, pp. 261–268 (1987)
Isard, M., Blake, A.: Contour Tracking by Stochastic Propagation of Conditional Density. In: European Conference on Computer Vision, vol. I, pp. 343–356 (1996)
Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: Algorithms based on the Hamilton-Jacobi formulation. Journal of Computational Physics 79, 12–49 (1988)
Osher, S., Paragios, N.: Geometric Level Set Methods in Imaging, Vision and Graphics. Springer, Heidelberg (2003)
Paragios, N., Deriche, R.: Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 266–280 (2000)
Cremers, D.: A Variational Framework for Image Segmentation Combining Motion Estimation and Shape Regularization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 53–58 (2003)
Sethian, J.: Level Set Methods. Cambridge University Press, Cambridge (1996)
Weber, M., Blake, A., Cipolla, R.: Sparse Finite Elements for Geodesic Contours with Level-Sets. In: European Conference on Computer Vision, pp. 391–404 (2004)
Paragios, N., Deriche, R.: Unifying Boundary and Region-based Information for Geodesic Active Tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. II-300–305 (1999)
Yezzi, A., Zollei, L., Kapur, T.: A Variational Framework for Joint Segmentation and Registration. IEEE Mathematical Methods in Biomedical Image Analysis, 44–51 (2001)
Dydenko, I., Friboulet, D., Magnin, I.: A Variational Framework for Affine Registration and Segmentation with Shape Prior: Application in Echocardiographic Imaging. In: Faugeras, O., Paragios, N. (eds.) IEEE Workshop in Variational and Level Set Methods, pp. 209–217 (2003)
Yilmaz, A., Li, X., Shah, B.: Contour based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
Sederberg, T., Parry, S.: Free-Form Deformation of Solid Geometric Models. In: ACM SIGGRAPH, vol. 4, pp. 151–160 (1986)
Faloutsos, P., van de Panne, M., Terzopoulos, D.: Dynamic Free-Form Deformations for Animation Synthesis. IEEE Transactions on Visualization and Computer Graphics 3, 201–214 (1997)
Zhao, H.-K., Chan, T., Merriman, B., Osher, S.: A variational Level Set Approach to Multiphase Motion. Journal of Computational Physics 127, 179–195 (1996)
Chan, T., Vese, L.: Active Contours without Edges. IEEE Transactions on Image Processing 10, 266–277 (2001)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. In: IEEE International Conference in Computer Vision, pp. 694–699 (1995)
Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., Yezzi, A.: Gradient flows and geometric active contour models. In: IEEE International Conference in Computer Vision, pp. 810–815 (1995)
Brox, T., Bruhn, A., Weickert, J.: High accuracy optical flow estiation based on a theory for warping. In: European Conference on Computer Vision, pp. 158–163 (2004)
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Karantzalos, K., Paragios, N. (2005). Implicit Free-Form-Deformations for Multi-frame Segmentation and Tracking. In: Paragios, N., Faugeras, O., Chan, T., Schnörr, C. (eds) Variational, Geometric, and Level Set Methods in Computer Vision. VLSM 2005. Lecture Notes in Computer Science, vol 3752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11567646_23
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DOI: https://doi.org/10.1007/11567646_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29348-4
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