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3D Optical Flow Estimation Combining 3D Census Signature and Total Variation Regularization

Abstract : We present a 3D optical flow method for 3D fluorescence image sequences which preserves discontinuities in the computed flow field. We propose to minimize an energy function composed of a linearized 3D Census signature-based data term and a total variational (TV) regularizer. To demonstrate the efficiency of our method, we have applied it to real sequences depicting collagen network, where the motion field is expected to be discontinuous. We also favorably compare our results with two other motion estimation methods.
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https://hal.inria.fr/hal-03085956
Contributor : Patrick Bouthemy <>
Submitted on : Tuesday, December 22, 2020 - 10:44:30 AM
Last modification on : Wednesday, December 23, 2020 - 3:35:17 AM

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Sandeep Manandhar, Patrick Bouthemy, Eric Welf, Philippe Roudot, Charles Kervrann. 3D Optical Flow Estimation Combining 3D Census Signature and Total Variation Regularization. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Apr 2020, Iowa City, France. pp.965-968, ⟨10.1109/ISBI45749.2020.9098690⟩. ⟨hal-03085956⟩

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