3D Optical Flow Estimation Combining 3D Census Signature and Total Variation Regularization - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

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

(1) , (1) , (2) , (2) , (1)
1
2

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.
Not file

Dates and versions

hal-03085956 , version 1 (22-12-2020)

Identifiers

Cite

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

Collections

INRIA INRIA2
38 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More