Abstract
Feature-based symmetry detection algorithms have become popular amongst researchers due to their dominance in performance, nevertheless, these approaches are computationally demanding. Also they are reliant on the presence of matched features, therefore they benefit from the abundance of detected keypoints; this implies that a trade-off between performance and computation time must be found. In this paper both issues are addressed, the detection of large sets of keypoints and the computation time for feature-based symmetry detection algorithms. We present an innovative process to learn rotation-invariant salient structures by clustering self-similarities. Keypoints are detected as local maxima in feature-maps computed using the learnt structures. Keypoints are described using BRISK. We consider an axis of symmetry to be a dense cloud of points in a parameter-space, a density-based clustering algorithm is used to find such clouds. Computing times are drastically shortened taking an average of 0.619 s to process an image. Detection results for single and multiple, straight and curved, reflection and glide-reflection symmetries are similar to the current state of the art.
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Heinly, J., Dunn, E., Frahm, J.-M.: Comparative evaluation of binary features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 759–773. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33709-3_54
Lee, S., Collins, R.T., Liu, Y.: Rotation symmetry group detection via frequency analysis of frieze-expansions. In: CVPR (2008)
Wang, Z., Tang, Z., Zhang, X.: Reflection symmetry detection using locally affine invariant edge correspondence. IEEE Trans. Image Process. 24, 1297–1301 (2015)
Brachmann, A., Redies, C.: Using convolutional neural network filters to measure left-right mirror symmetry in images. Symmetry 8, 144 (2016)
Yu, C.P., Ruppert, G.C.S., Nguyen, D.T.D., Falcao, A.X., Liu, Y.: Statistical asymmetry-based brain tumor segmentation from 3D MR images. In: Biosignals (2012)
Mancas, M., Gosselin, B., Macq, B., et al.: Fast and automatic tumoral area localisation using symmetry. In: ICASSP (2005)
Salti, S., Lanza, A., Di Stefano, L.: Keypoints from symmetries by wave propagation. In: CVPR (2013)
Levinshtein, A., Dickinson, S.J., Sminchisescu, C.: Multiscale symmetric part detection and grouping. In: ICCV (2009)
Teo, C.L., Fermuller, C., Aloimonos, Y.: Detection and segmentation of 2D curved reflection symmetric structures. In: ICCV (2015)
Liu, J., Slota, G., Zheng, G., Wu, Z., Park, M., Lee, S., Rauschert, I., Liu, Y.: Symmetry detection from real-world images competition 2013: summary and results. In: CVPR Workshops (2013)
Loy, G., Eklundh, J.-O.: Detecting symmetry and symmetric constellations of features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 508–521. Springer, Heidelberg (2006). doi:10.1007/11744047_39
Lee, S., Liu, Y.: Curved glide-reflection symmetry detection. In: TPAMI (2012)
Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: ICCV (2011)
Xiao, B., Ma, J.F., Wang, X.: Image analysis by Bessel-Fourier moments. Pattern Recogn. 43, 2620–2629 (2010)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)
Patraucean, V., Grompone von Gioi, R., Ovsjanikov, M.: Detection of mirror-symmetric image patches. In: CVPR Workshops (2013)
Kovesi, P., et al.: Symmetry and asymmetry from local phase. In: Tenth Australian Joint Conference on Artificial Intelligence (1997)
Kootstra, G., Nederveen, A., De Boer, B.: Paying attention to symmetry. In: BMVC (2008)
Liu, Y., Hel-Or, H., Kaplan, C.S.: Computational Symmetry in Computer Vision and Computer Graphics. Now Publishers Inc., Breda (2010)
Hauagge, D.C., Snavely, N.: Image matching using local symmetry features. In: CVPR (2012)
Kootstra, G., Schomaker, L.R.B.: Prediction of human eye fixations using symmetry. In: CogSci (2009)
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Lomeli-R., J., Nixon, M.S. (2017). Learning Salient Structures for the Analysis of Symmetric Patterns. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_32
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DOI: https://doi.org/10.1007/978-3-319-59876-5_32
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