Abstract
This paper deals with the extraction of multiple models from noisy, outlier-contaminated data. We build on the “preference trick” implemented by T-linkage, weakening the prior assumptions on the data: without requiring the tuning of the inlier threshold we develop a new automatic method which takes advantage of the geometric properties of Tanimoto space to bias the sampling toward promising models and exploits a density based analysis in the conceptual space in order to robustly estimate the models. Experimental validation proves that our method compares favourably with T-Linkage on public, real data-sets.
Chapter PDF
Similar content being viewed by others
References
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. In: ACM Sigmod Record, pp. 49–60 (1999)
Chin, T., Wang, H., Suter, D.: Robust fitting of multiple structures: the statistical learning approach. In: Int. Conf. on Computer Vision, pp. 413–420 (2009)
Chin, T.J., Yu, J., Suter, D.: Accelerated hypothesis generation for multistructure data via preference analysis. IEEE Trans. Pattern Anal. Mach. Intell., 533–546 (2012)
Chum, O., Matas, J.: Randomized ransac with T\(_{d, d}\) test. Image and Vision Computing 22, 837–842 (2002)
Chum, O., Matas, J.: Matching with PROSAC - progressive sample consensus. In: Int. Conf. on Computer Vision and Pattern Recognition, pp. 220–226 (2005)
Daszykowski, M., Walczak, B., Massart, D.L.: Looking for natural patterns in analytical data, 2. Tracing local density with OPTICS. Journal of Chemical Information and Computer Sciences 3, 500–507 (2002)
Elhamifar, E., Vidal, R.: Sparse subspace clustering: Algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765–2781 (2013)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Isack, H., Boykov, Y.: Energy-based geometric multi-model fitting. International Journal of Computer Vision 97(2), 123–147 (2012)
Kanazawa, Y., Kawakami, H.: Detection of planar regions with uncalibrated stereo using distribution of feature points. In: British Machine Vision Conf., pp. 247–256 (2004)
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell, 171–184 (2013)
Magri, L., Fusiello, A.: T-linkage: a continuous relaxation of J-linkage for multi-model fitting. In: Conf. on Computer Vision and Pattern Recognition, June 2014
Meyer, F., Beucher, S.: Morphological segmentation. Journal of Visual Communication and Image Representation 1(1), 21–46 (1990)
Soltanolkotabi, M., Elhamifar, E., Candès, E.J.: Robust subspace clustering. Ann. Statist. 42(2), 669–699 (2014)
Stewart, C.V.: Bias in robust estimation caused by discontinuities and multiple structures. IEEE Trans. Pattern Anal. Mach. Intell. 19(8), 818–833 (1997)
Toldo, R., Fusiello, A.: Robust multiple structures estimation with J-linkage. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 537–547. Springer, Heidelberg (2008)
Torr, P.H.S., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. Comp. Vis. and Image Underst. 1, 138–156 (2000)
Tron, R., Vidal, R.: A benchmark for the comparison of 3D motion segmentation algorithms. In: Conf. on Computer Vision and Pattern Recognition (2007)
Wong, H.S., Chin, T.J., Yu, J., Suter, D.: Dynamic and hierarchical multi-structure geometric model fitting. In: Int. Conf. on Computer Vision (2011)
Xu, L., Oja, E., Kultanen, P.: A new curve detection method: randomized Hough transform (RHT). Pattern Recognition Letters 11(5), 331–338 (1990)
Zhang, W., Kǒsecká, J.: Nonparametric estimation of multiple structures with outliers. In: Vidal, R., Heyden, A., Ma, Y. (eds.) WDV 2005/2006. LNCS, vol. 4358, pp. 60–74. Springer, Heidelberg (2007)
Zuliani, M., Kenney, C.S., Manjunath, B.S.: The multiRANSAC algorithm and its application to detect planar homographies. In: Int. Conf. on Image Processing (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Magri, L., Fusiello, A. (2015). Fitting Multiple Models via Density Analysis in Tanimoto Space. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9279. Springer, Cham. https://doi.org/10.1007/978-3-319-23231-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-23231-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23230-0
Online ISBN: 978-3-319-23231-7
eBook Packages: Computer ScienceComputer Science (R0)