Abstract
Aiming at the problem that the existing line segment detectors will detect overdense meaningless textures, this paper proposes a fusing contour features optimization method for line segment detector, called CF-Lines. We define a new line segment attribute, called "line segment associate contour(LAC)" attribute, which includes the contour features, the length and the angle of line segment. After using existing algorithms to detect contours and line segments, CF-Lines calculates the LAC of all line segments. When the LAC is greater than the threshold and passes the quantitative verification, the line segment is removed as overdense meaningless texture. Using the YorkUrban and Wireframe datasets, the CF-Lines is tested and compared with original detectors. Experimental results show that CF-Lines performs better than the original detectors in average precision, F-score, average length of line segments and average number of line segments.
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All data generated or analyzed during this study are available from the corresponding author on reasonable request.
References
Grinias I, Panagiotakis C, Tziritas G (2016) Mrf-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high -resolution satellite images. ISPRS J Photogram Remote Sens 122:145–166. https://doi.org/10.1016/j.isprsjprs.2016.10.010
Xu Y, Oh S, Hoogs A (2013) A minimum error vanishing point detection approach for uncalibrated monocular images of man-made environments. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp 1376–1383, https://doi.org/10.1109/CVPR.2013.181
Yu Z, Zheng J, Lian D, et al (2019) Single-image piece-wise planar 3d reconstruction via associative embedding. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 1029–1037, https://doi.org/10.1109/CVPR.2019.00112
De Brabandere B, Neven D, Van Gool L (2017) Semantic instance segmentation for autonomous driving. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 478–480, https://doi.org/10.1109/CVPRW.2017.66
Mukhopadhyay P, Chaudhuri BB (2015) A survey of hough transform. Pattern Recogn 48(3):993–1010. https://doi.org/10.1016/j.patcog.2014.08.027
Liu Y, Xie Z, Liu H (2019) Lb-lsd: a length-based line segment detector for real-time applications. Pattern Recogn Lett 128:247–254. https://doi.org/10.1016/j.patrec.2019.09.011
Galamhos C, Matas J, Kittler J (1999) Progressive probabilistic hough transform for line detection. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), pp 554–560 Vol. 1, https://doi.org/10.1109/CVPR.1999.786993
Fernandes LAF, Oliveira MM (2008) Real-time line detection through an improved hough transform voting scheme. Pattern Recogn 41(1):299–314. https://doi.org/10.1016/j.patcog.2007.04.003
Burns JB, Hanson AR, Riseman EM (1986) Extracting straight lines. IEEE Trans Pattern Anal Mach Intell PAMI 8(4):425–455. https://doi.org/10.1109/TPAMI.1986.4767808
Grompone von Gioi R, Jakubowicz J, Morel JM et al (2010) Lsd: a fast line segment detector with a false detection control. IEEE Trans Pattern Anal Mach Intell 32(4):722–732. https://doi.org/10.1109/TPAMI.2008.300
Topal C, Akinlar C (2012) Edge drawing: a combined real-time edge and segment detector. J Vis Commun Image Rep 23(6):862–872. https://doi.org/10.1016/j.jvcir.2012.05.004
Cho NG, Yuille A, Lee SW (2018) A novel linelet-based representation for line segment detection. IEEE Trans Pattern Anal Mach Intell 40(5):1195–1208. https://doi.org/10.1109/TPAMI.2017.2703841
Almazàn EJ, Tal R, Qian Y, et al (2017) Mcmlsd: A dynamic programming approach to line segment detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5854–5862, https://doi.org/10.1109/CVPR.2017.620
Suárez I, Muñoz E, Buenaposada JM, et al (2018) Fsg: A statistical approach to line detection via fast segments grouping. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 97–102, https://doi.org/10.1109/IROS.2018.8594434
Liu C, Abergel R, Gousseau Y et al (2020) Lsdsar, a markovian a contrario framework for line segment detection in sar images. Pattern Recogn 98:107034. https://doi.org/10.1016/j.patcog.2019.107034
Zhang X, Hu C, Liu H et al (2023) A line segment detector for space target images robust to complex illumination. Aerospace 10(2):195. https://doi.org/10.3390/aerospace10020195
Xue N, Bai S, Wang F, et al (2019) Learning attraction field representation for robust line segment detection. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Proceedings, pp 1595–603, https://doi.org/10.1109/CVPR.2019.00169
Huang S, Qin F, Xiong P, et al (2020) Tp-lsd: tri-points based line segment detector. In: Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12372), pp 770–85, https://doi.org/10.1007/978-3-030-58583-9_46
Pautrat R, Lin JT, Larsson V, et al (2021) Sold2: self-supervised occlusion-aware line description and detection. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, pp 11363–11373, https://doi.org/10.1109/CVPR46437.2021.01121
Xu Y, Xu W, Cheung D, et al (2021) Line segment detection using transformers without edges. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, pp 4255–4264, https://doi.org/10.1109/CVPR46437.2021.00424
Gong XY, Su H, Xu D et al (2018) An overview of contour detection approaches. Int J Autom Comput 15:656–672. https://doi.org/10.1007/s11633-018-1117-z
Grigorescu C, Petkov N, Westenberg M (2003) Contour detection based on nonclassical receptive field inhibition. IEEE Trans Image Process 12(7):729–739. https://doi.org/10.1109/TIP.2003.814250
Grigorescu C, Petkov N, Westenberg M (2004) Contour and boundary detection improved by surround suppression of texture edges. Image Vision Comput 22(8):609–622. https://doi.org/10.1016/j.imavis.2003.12.004
Petkov N, Westenberg M (2003) Suppression of contour perception by band-limited noise and its relation to nonclassical receptive field inhibition. Biol Cybern 88(3):236–246. https://doi.org/10.1007/s00422-002-0378-2
Cox I, Rehg J, Hingorani S (1993) A bayesian multiple-hypothesis approach to edge grouping and contour segmentation. Int J Comput Vision 11(1):5–24. https://doi.org/10.1007/BF01420590
Elder J, Zucker S (1996) Computing contour closure. In: Computer Vision - ECCV ‘96. 4th Eurpean Conference on Computer Proceedings, pp 399–412, https://doi.org/10.1007/BFb0015553
Geisler W, Perry J, Super B et al (2001) Edge co-occurrence in natural images predicts contour grouping performance. Vision Res 41(6):711–724. https://doi.org/10.1016/S0042-6989(00)00277-7
Arbelaez P, Maire M, Fowlkes C, et al (2009) From contours to regions: An empirical evaluation. In: CVPR: 2009 IEEE Conference on Computer Vision and Pattern Recognition, VOLS 1-4, p 2294-2301, https://doi.org/10.1109/CVPR.2009.5206707
Ming Y, Li H, He X (2015) Winding number constrained contour detection. IEEE Trans Image Process 24(1):68–79. https://doi.org/10.1109/TIP.2014.2372636
Shen W, Wang X, Wang Y, et al (2015) Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3982–3991, https://doi.org/10.1109/CVPR.2015.7299024
Bertasius G, Shi J, Torresani L (2015) Deepedge: a multi-scale bifurcated deep network for top-down contour detection deepedge: A multi-scale bifurcated deep network for top-down contour detection. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4380–4389, https://doi.org/10.1109/CVPR.2015.7299067
Hariharan B, Arbelaez P, Bourdev L, et al (2011) Semantic contours from inverse detectors. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp 991–998, https://doi.org/10.1109/ICCV.2011.6126343
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI 8(6):679–698. https://doi.org/10.1109/TPAMI.1986.4767851
Luo K, Deng J, Cai W, et al (2022) Line segment extraction algorithm optimization based on shi-tomasi corner detector. J Sounth China Normal Univ (Natural Science Edition) 54(1):113–121
Denis P, Elder JH, Estrada FJ (2008) Efficient edge-based methods for estimating manhattan frames in urban imagery. Computer Vision - ECCV 2008. PT II, Proceedings, pp 197–210
Huang K, Wang Y, Zhou Z, et al (2018) Learning to parse wireframes in images of man-made environments. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 626–635, https://doi.org/10.1109/CVPR.2018.00072
Wang J, Wang C, Huang T (2013) Efficient image contour detection using edge prior. In: 2013 IEEE International Conference on Multimedia and Expo (ICME 2013)
Elliott DL (1993) A better activation function for artificial neural networks. Technical Research Report
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This work was supported by the National Natural Science Foundation of China (NO.U1911401).
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Conceptualization: Runsheng Liu and Wencong Cai; Methodology: Runsheng Liu, Wencong Cai and Junyang Zhang; Formal analysis and investigation: Xiaoling Wu and Lilin Yang; Writing - original draft preparation: Runsheng Liu; Writing - review and editing: Wencong Cai, Junyang Zhang and Lilin Yang; Funding acquisition: Kaiqing Luo; Resources: Kaiqing Luo; Supervision: Kaiqing Luo and Xiaoling Wu.
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Liu, R., Cai, W., Zhang, J. et al. CF-lines: a fusing contour features optimization method for line segment detector. J Supercomput 80, 3644–3662 (2024). https://doi.org/10.1007/s11227-023-05615-3
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DOI: https://doi.org/10.1007/s11227-023-05615-3