Abstract
In this paper, we provide a real-time image deformation method based on Locally-weighted Moving Least Squares (LW-MLS). To achieve a detail-preserving and realistic deformation of images, a concise deformation formula is proposed as the deformation function. Compared with two state-of-the-art methods, Moving Least Squares (MLS) and Moving Regularized Least Squares (MRLS), the main improvement of our method is preprocessing the control points, which adopts sparse approximation to achieve a fast deformation. With the traditional methods of image deformation, each pixel is affected by all control points, which consume too much time to deform an image. So in our method, each pixel is mainly affected by surrounding control points, and every pixel is almost not affected by the control points which are far away from the deformed pixel. The novel method we proposed can be performed in real time and could supply promising performance for the deformation of large image.
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References
Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 567–585 (1989)
Chen, X., Xie, Q., Shen, L., Han, H.: Wrinkle image registration for serial microscopy sections. In: International Conference on Signal-Image Technology & Internet-Based Systems, pp. 23–26 (2016)
Ju, T., Warren, J., Eichele, G., Thaller, C., Chiu, W., Carson, J.: A geometric database for gene expression data. Symp. Geom. Process. 2003, 166–176 (2003)
Kasthuri, N., et al.: Saturated reconstruction of a volume of neocortex. Cell 162(3), 648–661 (2015)
Li, L., Li, W., Zou, B., Wang, Y., Tang, Y., Han, H.: Learning with coefficient-based regularized regression on Markov resampling. IEEE Trans. Neural Netw. Learn. Syst. (2017)
Li, W., Deng, H., Rao, Q., Xie, Q., Chen, X., Han, H.: An automated pipeline for mitochondrial segmentation on atum-sem stacks. J. Bioinform. Comput. Biol. 15(3), 1750015 (2017)
Lichtman, J.W., Pfister, H., Shavit, N.: The big data challenges of connectomics. Nat. Neurosci. 17(11), 1448–1454 (2014)
Ma, J., Zhao, J., Tian, J.: Nonrigid image deformation using moving regularized least squares. IEEE Signal Process. Lett. 20(10), 988–991 (2013)
Ma, J., Zhao, J., Tian, J., Tu, Z., Yuille, A.L.: Robust estimation of nonrigid transformation for point set registration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2147–2154 (2013)
Maccracken, R., Joy, K.I.: Free-form deformations with lattices of arbitrary topology. In: Conference on Computer Graphics and Interactive Techniques, pp. 181–188 (1996)
Marblestone, A.H., et al.: Conneconomics: the economics of large-scale neural connectomics, pp. 337–349 (2013)
Qiao, T., et al.: Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis. IEEE Trans. Geosci. Remote Sens. 55(99), 1–15 (2017)
Saitoh, S.: Theory of reproducing kernels. Trans. Am. Math. Soc. 68(3), 337–404 (2003)
Schaefer, S., Mcphail, T., Warren, J.: Image deformation using moving least squares. In: ACM SIGGRAPH, pp. 533–540 (2006)
Thompson, P., Toga, A.W.: A surface-based technique for warping three-dimensional images of the brain. IEEE Trans. Med. Imaging 15(4), 402 (1996)
Tsai, Y.C., Lin, H.D., Hu, Y.C., Yu, C.L., Lin, K.P.: Thin-plate spline technique for medical image deformation. J. Med. Biol. Eng. 20(4), 203–210 (2000)
Wittek, A., Miller, K., Kikinis, R., Warfield, S.K.: Patient-specific model of brain deformation: application to medical image registration. J. Biomech. 40(4), 919–929 (2007)
Yan, Y., Ren, J., Li, Y., Windmill, J.F.C., Ijomah, W., Chao, K.M.: Adaptive fusion of color and spatial features for noise-robust retrieval of colored logo and trademark images. Multidimens. Syst. Signal Process. 27(4), 1–24 (2016)
Acknowledgments
This paper is supported by National Science Foundation of China (No. 11771130, 61673381, 61201050, 61701497), Scientific Instrument Developing Project of Chinese Academy of Sciences (No. YZ201671), Bureau of International Cooperation, CAS (No. 153D31KYSB20170059), and Special Program of Beijing Municipal Science & Technology Commission (No. Z161100000216146).
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Zhao, L., Chen, X., Shu, C., Yu, C., Han, H. (2018). Real-Time Image Deformation Using Locally-Weighted Moving Least Squares. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_69
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DOI: https://doi.org/10.1007/978-3-030-00563-4_69
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