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3D Optical Flow Estimation Combining 3D Census Signature and Total Variation Regularization
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3D Optical Flow Estimation Combining 3D Census Signature and Total Variation Regularization
We present a 3D variational optical flow method for 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 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.
We present a 3D variational optical flow method for 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 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.
