| ECVision indexed and annotated bibliography of cognitive computer vision publications This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1 The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
L. Alvarez and R. Deriche and T. Papadopoulo and J. Sanchez
ABSTRACT
Traditional techniques of dense optical .owestimation don’t generally yield symmetrical solutions: the results will di.er if they are applied between images I1 and I2 or between images I2 and I1. In this work, we present a method to recover a dense optical .ow .eld map from two images, while explicitely taking into account the symmetry across the images as well as possible occlusions and discontinuities in the .ow .eld. The idea is to consider both displacements vectors from I1 to I2 and I2 to I1 and to minimise an energy functional that explicitely encodes all those properties. This variational problem is then solved using the gradient .owde.ned by the Euler–Lagrange equations associated to the energy. In order to reduce the risk to be trapped within some irrelevant minimum, a focusing strategy based on a multi-resolution technique is used to converge toward the solution. Promising experimental results on both synthetic and real images are presented to illustrate the capabilities of this symmetrical variational approach to recover accurate optical .ow. |
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