CLKN: Cascaded Lucas-Kanade Networks for Image Alignment

This paper proposes a data-driven approach for image alignment. Our main contribution is a novel network architecture that combines the strengths of convolutional neural networks (CNNs) and the Lucas-Kanade algorithm. The main component of this architecture is a Lucas-Kanade layer that performs the...

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Bibliographic Details
Published in:2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 3777 - 3785
Main Authors: Che-Han Chang, Chun-Nan Chou, Chang, Edward Y.
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2017
Subjects:
Estimation
Feature extraction
Jacobian matrices
Lighting
Mathematical model
Neural networks
Robustness
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary:This paper proposes a data-driven approach for image alignment. Our main contribution is a novel network architecture that combines the strengths of convolutional neural networks (CNNs) and the Lucas-Kanade algorithm. The main component of this architecture is a Lucas-Kanade layer that performs the inverse compositional algorithm on convolutional feature maps. To train our network, we develop a cascaded feature learning method that incorporates the coarse-to-fine strategy into the training process. This method learns a pyramid representation of convolutional features in a cascaded manner and yields a cascaded network that performs coarse-to-fine alignment on the feature pyramids. We apply our model to the task of homography estimation, and perform training and evaluation on a large labeled dataset generated from the MS-COCO dataset. Experimental results show that the proposed approach significantly outperforms the other methods.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2017.402