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http://dx.doi.org/10.5626/JOK.2016.43.9.1034

Fast and All-Purpose Area-Based Imagery Registration Using ConvNets  

Baek, Seung-Cheol (ADD)
Publication Information
Journal of KIISE / v.43, no.9, 2016 , pp. 1034-1042 More about this Journal
Abstract
Together with machine-learning frameworks, area-based imagery registration techniques can be easily applied to diverse types of image pairs without predefined features and feature descriptors. However, feature detectors are often used to quickly identify candidate image patch pairs, limiting the applicability of these registration techniques. In this paper, we propose a ConvNet (Convolutional Network) "Dart" that provides not only the matching metric between patches, but also information about their distance, which are helpful in reducing the search space of the corresponding patch pairs. In addition, we propose a ConvNet "Fad" to identify the patches that are difficult for Dart to improve the accuracy of registration. These two networks were successfully implemented using Deep Learning with the help of a number of training instances generated from a few registered image pairs, and were successfully applied to solve a simple image registration problem, suggesting that this line of research is promising.
Keywords
imagery registration; convolutional network; deep learning; image processing; ConvNet;
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1 S. Saxena and R. Singh, "A Survey of Recent and Classical Image Registration Methods," International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 7, No. 4, pp. 167-176, 2014.   DOI
2 F. Oliveira and J. Tavares, "Medical image registration: a review," Computer Methods in Biomechanics and Biomedical Engineering, Vol. 17, No. 2, pp. 73-93, Mar. 2014.   DOI
3 Y. LeCun, Y. Bengion and G. Hinton, "Deep learning," Nature, Vol. 521, pp. 436-444, May. 2015.   DOI
4 O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. Berg, and L. Fei-Fei "ImageNet Large Scale Visual Recognition Challenge," International Journal of Computer Vision, Vol. 115, pp. 211-252, Apr. 2015.   DOI
5 P. Fisher, A. Dosovitskiy, and T. Brox (2014, May 22) Descriptor matching with convolutional neural networks: a comparison to SIFT [Online]. Available: https://arxiv.org/abs/1405.5769 (downloaded 2014, May 22).
6 J. Zbontar and Y. LeCun, "Computing the stereo matching cost with a convolutional neural network," Proc. of Computer Vision and Pattern Recognition 2015, pp. 1592-1599, 2015.
7 S. Zagorukyo and N. Komodakis, "Learning to Compare Image Patches via Convolutional Neural Networks," Proc. of Computer Vision and Pattern Recognition 2015, pp. 4353-4361, 2015.
8 G. Wu, M. Kim, Q. Wang, y. Gao, S. Liao and D. Shen, "Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images," Medical Image Computing, Computer-Asisted Intervention, Vol. 16, No. 2, pp. 649-656, Jun. 2013.
9 D. Lowe, "Object recognition from local scale-invariant features," Proc. of the Internatl Conference on Computer Vision, pp. 150-1157, 1999.
10 H. Vay, A. Ess, T. Tuytelaars and L. Van Gool, "SURF: Speeded Up Robust Features," Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346-359, 2008.   DOI
11 K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptor," IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 27, No. 10, pp. 1615-1630, 2005.   DOI
12 K, He, X. Zhang, S. Ren, and J. Sun Y., "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification," Proc. of the IEEE International Conference on Computer Vision, 2015.
13 Y. Nesterov, "Gradient methods for minimizing composite functions," Mathematical Programming, Vol. 140, No. 1, pp. 125-161, 2013.   DOI
14 M. Kumar, B. Packer, D. Koller, "Self-Paced Learning for Latent Variable Models," Advances in Neural Information Processing Systems, pp. 1189-1197, 2010.