367 research outputs found
Improving wordspotting performance with limited training data
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 149-155).by Eric I-Chao Chang.Ph.D
Recursive Cascaded Networks for Unsupervised Medical Image Registration
We present recursive cascaded networks, a general architecture that enables
learning deep cascades, for deformable image registration. The proposed
architecture is simple in design and can be built on any base network. The
moving image is warped successively by each cascade and finally aligned to the
fixed image; this procedure is recursive in a way that every cascade learns to
perform a progressive deformation for the current warped image. The entire
system is end-to-end and jointly trained in an unsupervised manner. In
addition, enabled by the recursive architecture, one cascade can be iteratively
applied for multiple times during testing, which approaches a better fit
between each of the image pairs. We evaluate our method on 3D medical images,
where deformable registration is most commonly applied. We demonstrate that
recursive cascaded networks achieve consistent, significant gains and
outperform state-of-the-art methods. The performance reveals an increasing
trend as long as more cascades are trained, while the limit is not observed.
Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.Comment: Accepted to ICCV 201
MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask
Feature warping is a core technique in optical flow estimation; however, the
ambiguity caused by occluded areas during warping is a major problem that
remains unsolved. In this paper, we propose an asymmetric occlusion-aware
feature matching module, which can learn a rough occlusion mask that filters
useless (occluded) areas immediately after feature warping without any explicit
supervision. The proposed module can be easily integrated into end-to-end
network architectures and enjoys performance gains while introducing negligible
computational cost. The learned occlusion mask can be further fed into a
subsequent network cascade with dual feature pyramids with which we achieve
state-of-the-art performance. At the time of submission, our method, called
MaskFlownet, surpasses all published optical flow methods on the MPI Sintel,
KITTI 2012 and 2015 benchmarks. Code is available at
https://github.com/microsoft/MaskFlownet.Comment: CVPR 2020 (Oral
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