A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges (2305.00264v2)
Abstract: An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portions of objects and scenarios within images. Detection and description of line segments lay the basis for numerous vision tasks. Although many studies have aimed to detect and describe line segments, a comprehensive review is lacking, obstructing their progress. This study fills the gap by comprehensively reviewing related studies on detecting and describing two-dimensional image line segments to provide researchers with an overall picture and deep understanding. Based on their mechanisms, two taxonomies for line segment detection and description are presented to introduce, analyze, and summarize these studies, facilitating researchers to learn about them quickly and extensively. The key issues, core ideas, advantages and disadvantages of existing methods, and their potential applications for each category are analyzed and summarized, including previously unknown findings. The challenges in existing methods and corresponding insights for potentially solving them are also provided to inspire researchers. In addition, some state-of-the-art line segment detection and description algorithms are evaluated without bias, and the evaluation code will be publicly available. The theoretical analysis, coupled with the experimental results, can guide researchers in selecting the best method for their intended vision applications. Finally, this study provides insights for potentially interesting future research directions to attract more attention from researchers to this field.
- T. Tuytelaars and K. Mikolajczyk, “Local invariant feature detectors: A survey,” Found. Trends Comput. Graph. Vis., vol. 3, no. 3, pp. 177–280, 2008.
- R. Grompone von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, “Lsd: A fast line segment detector with a false detection control,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 4, pp. 722–732, 2010.
- D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, p. 91–110, 2004.
- E. Rosten, R. Porter, and T. Drummond, “Faster and better: A machine learning approach to corner detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 105–119, 2010.
- A. Barroso-Laguna and K. Mikolajczyk, “Key.net: Keypoint detection by handcrafted and learned cnn filters revisited,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 1, pp. 698–711, 2023.
- J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698, 1986.
- C. Luengo Hendriks and L. van Vliet, “Using line segments as structuring elements for sampling-invariant measurements,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 11, pp. 1826–1831, 2005.
- X. Lin, Y. Zhou, X. Zhang, Y. Liu, and C. Zhu, “Efficient and effective multi-camera pose estimation with weighted m-estimate sample consensus,” in IEEE Int. Conf. Acoust. Speech Signal Process., Rhodes Island, Greece, Jun. 2023.
- X. Lin, Y. Zhou, Y. Liu, and C. Zhu, “Long-term visual localization using illumination insensitive descriptors,” in IEEE Int. Workshop Multimed. Signal Process., Shanghai, China, Sep. 2022, pp. 1–1.
- C. Xu, L. Zhang, L. Cheng, and R. Koch, “Pose estimation from line correspondences: A complete analysis and a series of solutions,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1209–1222, 2017.
- E. J. Shamwell, K. Lindgren, S. Leung, and W. D. Nothwang, “Unsupervised deep visual-inertial odometry with online error correction for rgb-d imagery,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 10, pp. 2478–2493, 2020.
- K. Joo, P. Kim, M. Hebert, I. S. Kweon, and H. J. Kim, “Linear rgb-d slam for structured environments,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 11, pp. 8403–8419, 2022.
- L. Magerand and A. Del Bue, “Revisiting projective structure from motion: A robust and efficient incremental solution,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 430–443, 2020.
- C. Akinlar and C. Topal, “Edlines: A real-time line segment detector with a false detection control,” Pattern Recognit. Lett., vol. 32, no. 13, pp. 1633–1642, 2011.
- C. Topal and C. Akinlar, “Edge drawing: A combined real-time edge and segment detector,” J. Vis. Commun. Image Represent., vol. 23, no. 6, pp. 862–872, 2012.
- J. Jing, T. Gao, W. Zhang, Y. Gao, and C. Sun, “Image feature information extraction for interest point detection: A comprehensive review,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 4, pp. 4694–4712, 2023.
- J. Jing, S. Liu, G. Wang, W. Zhang, and C. Sun, “Recent advances on image edge detection: A comprehensive review,” Neurocomputing, vol. 503, pp. 259–271, 2022.
- K. Ivanov, G. Ferrer, and A. Kornilova, “Evolin benchmark: Evaluation of line detection and association,” 2023, arXiv:2303.05162.
- P. V. Hough, “Method and means for recognizing complex patterns,” Dec. 1962, uS Patent 3,069,654.
- R. O. Duda and P. E. Hart, “Use of the hough transformation to detect lines and curves in pictures,” Commun. ACM, vol. 15, no. 1, p. 11–15, 1972.
- D. Ballard, “Generalizing the hough transform to detect arbitrary shapes,” Pattern Recognit., vol. 13, no. 2, pp. 111–122, 1981.
- I. Svalbe, “Natural representations for straight lines and the hough transform on discrete arrays,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 9, pp. 941–950, 1989.
- S. R. Deans, “Hough transform from the radon transform,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-3, no. 2, pp. 185–188, 1981.
- M. Atiquzzaman and M. W Akhtar, “Complete line segment description using the hough transform,” Image Vis. Comput., vol. 12, no. 5, pp. 267–273, 1994.
- N. Guil, J. Villalba, and E. Zapata, “A fast hough transform for segment detection,” IEEE Trans. Image Process., vol. 4, no. 11, pp. 1541–1548, 1995.
- M. Yang, J.-S. Lee, C.-C. Lien, and C.-L. Huang, “Hough transform modified by line connectivity and line thickness,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 8, pp. 905–910, 1997.
- V. Kamat-Sadekar and S. Ganesan, “Complete description of multiple line segments using the hough transform,” Image Vis. Comput., vol. 16, no. 9, pp. 597–613, 1998.
- J. Matas, C. Galambos, and J. Kittler, “Robust detection of lines using the progressive probabilistic hough transform,” Comput. Vis. Image Underst., vol. 78, no. 1, pp. 119–137, 2000.
- Y. Furukawa and Y. Shinagawa, “Accurate and robust line segment extraction by analyzing distribution around peaks in hough space,” Comput. Vis. Image Underst., vol. 92, no. 1, p. 1–25, 2003.
- J. Cha, R. Cofer, and S. Kozaitis, “Extended hough transform for linear feature detection,” Pattern Recognit., vol. 39, no. 6, pp. 1034–1043, 2006.
- R. Grompone von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, “On straight line segment detection,” J. Math. Imaging Vis., vol. 32, no. 3, pp. 313–347, 2008.
- S. Du, B. J. van Wyk, C. Tu, and X. Zhang, “An improved hough transform neighborhood map for straight line segments,” IEEE Trans. Image Process., vol. 19, no. 3, pp. 573–585, 2010.
- S. Du, C. Tu, B. J. van Wyk, and Z. Chen, “Collinear segment detection using ht neighborhoods,” IEEE Trans. Image Process., vol. 20, no. 12, pp. 3612–3620, 2011.
- R. F. C. Guerreiro and P. M. Q. Aguiar, “Connectivity-enforcing hough transform for the robust extraction of line segments,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4819–4829, 2012.
- R. Tal and J. H. Elder, “An accurate method for line detection and manhattan frame estimation,” in Asian Conference on Computer Vision, Daejeon, Korea, Nov. 2012, pp. 580–593.
- Z. Xu, B.-S. Shin, and R. Klette, “A statistical method for line segment detection,” Comput. Vis. Image Underst., vol. 138, no. C, p. 61–73, 2015.
- Z. Xu, B.-S. Shin, and R. Klette, “Accurate and robust line segment extraction using minimum entropy with hough transform,” IEEE Trans. Image Process., vol. 24, no. 3, pp. 813–822, 2015.
- Z. Xu, B.-S. Shin, and R. Klette, “Closed form line-segment extraction using the hough transform,” Pattern Recognit., vol. 48, no. 12, p. 4012–4023, 2015.
- P. Bachiller-Burgos, L. J. Manso, and P. Bustos, “A variant of the hough transform for the combined detection of corners, segments, and polylines,” EURASIP J. Image Video Process., vol. 2017, no. 1, pp. 1–26, 2017.
- M. Dubská, A. Herout, and J. Havel, “Pclines — line detection using parallel coordinates,” in IEEE Conf. Comput. Vis. Pattern Recog., Colorado Springs, CO, USA, Jun. 2011, pp. 1489–1494.
- J. B. Burns, A. R. Hanson, and E. M. Riseman, “Extracting straight lines,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 4, pp. 425–455, 1986.
- M. Boldt, R. Weiss, and E. Riseman, “Token-based extraction of straight lines,” IEEE Trans. Syst., Man, Cybern., vol. 19, no. 6, pp. 1581–1594, 1989.
- R. Nelson, “Finding line segments by stick growing,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 5, pp. 519–523, 1994.
- R. Grompone von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, “Lsd: a line segment detector,” Image Process. Line, vol. 2, pp. 35–55, 2012.
- M. Nieto, C. Cuevas, L. Salgado, and N. García, “Line segment detection using weighted mean shift procedures on a 2d slice sampling strategy,” Pattern Anal. Appl., vol. 14, no. 2, pp. 149–163, 2011.
- V. PăźTrăźUcean, P. Gurdjos, and R. G. Gioi, “A parameterless line segment and elliptical arc detector with enhanced ellipse fitting,” in Eur. Conf. Comput. Vis., Florence, Italy, Oct. 2012, p. 572–585.
- Y. Salaün, R. Marlet, and P. Monasse, “Multiscale line segment detector for robust and accurate sfm,” in Int. Conf. Pattern Recog., Cancun, Mexico, Dec. 2016, pp. 2000–2005.
- V. Pătrăucean, P. Gurdjos, and R. Grompone von Gioi, “Joint a contrario ellipse and line detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 788–802, 2017.
- N.-G. Cho, A. Yuille, and S.-W. Lee, “A novel linelet-based representation for line segment detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 5, pp. 1195–1208, 2018.
- I. Suárez, E. Muñoz, J. M. Buenaposada, and L. Baumela, “Fsg: A statistical approach to line detection via fast segments grouping,” in IEEE Int. Conf. Intell. Robots Syst., Madrid, Spain, Oct. 2018, pp. 97–102.
- Q. Yu, G. Xu, Y. Cheng, and Z. H. Zhu, “Plsd: A perceptually accurate line segment detection approach,” IEEE Access, vol. 8, pp. 42 595–42 607, 2020.
- Y. Zhang, D. Wei, and Y. Li, “Ag3line: Active grouping and geometry-gradient combined validation for fast line segment extraction,” Pattern Recognit., vol. 113, p. 107834, 2021.
- D. Wang, Q. Liu, Q. Yin, and F. Ma, “Fast line segment detection and large scene airport detection for polsar,” Remote Sens., vol. 14, no. 22, 2022.
- X. Zhang, C. Hu, H. Liu, R. Du, X. Zhou, and L. Wang, “A line segment detector for space target images robust to complex illumination,” Aerospace, vol. 10, no. 2, 2023.
- V. Venkateswar and R. Chellappa, “Extraction of straight lines in aerial images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 11, pp. 1111–1114, 1992.
- D. S. Guru, B. H. Shekar, and P. Nagabhushan, “A simple and robust line detection algorithm based on small eigenvalue analysis,” Pattern Recognit. Lett., vol. 25, no. 1, p. 1–13, 2004.
- K. Yang, S. Sam Ge, and H. He, “Robust line detection using two-orthogonal direction image scanning,” Comput. Vis. Image Underst., vol. 115, no. 8, pp. 1207–1222, 2011.
- J. H. Lee, S. Lee, G. Zhang, J. Lim, W. K. Chung, and I. H. Suh, “Outdoor place recognition in urban environments using straight lines,” in IEEE Int. Conf. Robot. Autom., Hong Kong, China, Jun. 2014, pp. 5550–5557.
- X. Lu, J. Yao, K. Li, and L. Li, “Cannylines: A parameter-free line segment detector,” in IEEE Int. Conf. Image Process., Quebec City, QC, Canada, Sep. 2015, pp. 507–511.
- W. Ding, W. Wang, and X. Li, “Otlines: A novel line-detection algorithm without the interference of smooth curves,” Pattern Recognit., vol. 53, pp. 238–258, 2016.
- Y. Liu, Z. Xie, and H. Liu, “Lb-lsd: A length-based line segment detector for real-time applications,” Pattern Recognit. Lett., vol. 128, pp. 247–254, 2019.
- J. Deng, J. Cheng, W. Cai, Y. Zhou, R. Fan, and K. Luo, “Property similarity line segment detector,” in IEEE Int. Conf. Imaging Syst. Techniques, Kaohsiung, Taiwan, Aug. 2021.
- I. Suárez, J. M. Buenaposada, and L. Baumela, “Elsed: Enhanced line segment drawing,” Pattern Recognit., vol. 127, p. 108619, 2022.
- I. C. Yilmaz and I. C. Baykal, “Ultrafast line detector,” J. Electron. Imaging, vol. 31, no. 4, p. 043019, 2022.
- X. Lin, Y. Zhou, Y. Liu, and C. Zhu, “Level-line guided edge drawing for robust line segment detection,” in IEEE Int. Conf. Acoust. Speech Signal Process., Rhodes Island, Greece, Jun. 2023.
- X. Lin, Y. Zhou, Y. Liu, and C. Zhu, “Effective and efficient line segment detection for visual measurement guided by level lines,” IEEE Trans. Instrum. Meas., vol. 72, pp. 1–12, 2023.
- C. Liu, C. Liu, C. Wang, W. Zhu, and Q. Li, “A novel pixel orientation estimation based line segment detection framework, and its applications to sar images,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–19, 2022.
- Y. Sun, X. Han, K. Sun, B. Li, Y. Chen, and M. Li, “Sem-lsd: A learning-based semantic line segment detector,” 2019, arXiv:1909.06591.
- N. Xue, S. Bai, F. Wang, G.-S. Xia, T. Wu, and L. Zhang, “Learning attraction field representation for robust line segment detection,” in IEEE Conf. Comput. Vis. Pattern Recog., Long Beach, CA, USA, Jun. 2019, pp. 1595–1603.
- Z. Xue, N. Xue, G.-S. Xia, and W. Shen, “Learning to calibrate straight lines for fisheye image rectification,” in IEEE Conf. Comput. Vis. Pattern Recog., Long Beach, CA, USA, Jun. 2019, pp. 1643–1651.
- S. Huang, F. Qin, P. Xiong, N. Ding, Y. He, and X. Liu, “Tp-lsd: Tri-points based line segment detector,” in Eur. Conf. Comput. Vis., Glasgow, UK, Aug. 2020, pp. 770–785.
- Q. Meng, J. Zhang, Q. Hu, X. He, and J. Yu, “Lgnn: A context-aware line segment detector,” in Proc. ACM Multimed. Conf., New York, NY, USA, Oct. 2020, p. 4364–4372.
- H. Abdellali, R. Frohlich, V. Vilagos, and Z. Kato, “L2d2: Learnable line detector and descriptor,” in Int. Conf. 3D Vis., London, United Kingdom, Dec. 2021, pp. 442–452.
- X. Dai, H. Gong, S. Wu, X. Yuan, and M. Yi, “Fully convolutional line parsing,” Neurocomputing, vol. 506, pp. 1–11, 2022.
- G. Gu, B. Ko, S. Go, S.-H. Lee, J. Lee, and M. Shin, “Towards light-weight and real-time line segment detection,” in AAAI Conference on Artificial Intelligence, Feb. 2022, pp. 726–734.
- H. Li, H. Yu, J. Wang, W. Yang, L. Yu, and S. Scherer, “Ulsd: Unified line segment detection across pinhole, fisheye, and spherical cameras,” ISPRS J. Photogramm. Remote Sens., vol. 178, pp. 187–202, 2021.
- R. Pautrat, J.-T. Lin, V. Larsson, M. R. Oswald, and M. Pollefeys, “Sold2: Self-supervised occlusion-aware line description and detection,” in IEEE Conf. Comput. Vis. Pattern Recog., Nashville, TN, USA, Jun. 2021, pp. 11 363–11 373.
- C. Qiao, T. Bai, Z. Xiang, Q. Qian, and Y. Bi, “Superline: A robust line segment feature for visual slam,” in IEEE Int. Conf. Intell. Robots Syst., Prague, Czech Republic, Sep. 2021, pp. 5664–5670.
- Y. Xu, W. Xu, D. Cheung, and Z. Tu, “Line segment detection using transformers without edges,” in IEEE Conf. Comput. Vis. Pattern Recog., Nashville, TN, USA, Jun. 2021, pp. 4255–4264.
- N. Xue, S. Bai, F.-D. Wang, G.-S. Xia, T. Wu, L. Zhang, and P. H. Torr, “Learning regional attraction for line segment detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 6, pp. 1998–2013, 2021.
- H. Zhang, Y. Luo, F. Qin, Y. He, and X. Liu, “Elsd: Efficient line segment detector and descriptor,” in Int. Conf. Comput. Vis., Montreal, QC, Canada, Oct. 2021, pp. 2949–2958.
- J. Huang, X. Lu, M. Yu, and F. Li, “Self-supervised lightweight line segment detector and descriptor,” in 2022 China Automation Congress (CAC), Xiamen, China, Nov. 2022, pp. 5263–5268.
- K. Huang, Y. Wang, Z. Zhou, T. Ding, S. Gao, and Y. Ma, “Learning to parse wireframes in images of man-made environments,” in IEEE Conf. Comput. Vis. Pattern Recog., Salt Lake City, UT, USA, Jun. 2018, pp. 626–635.
- K. Huang and S. Gao, “Wireframe parsing with guidance of distance map,” IEEE Access, vol. 7, pp. 141 036–141 044, 2019.
- Z. Zhang, Z. Li, N. Bi, J. Zheng, J. Wang, K. Huang, W. Luo, Y. Xu, and S. Gao, “Ppgnet: Learning point-pair graph for line segment detection,” in IEEE Conf. Comput. Vis. Pattern Recog., Long Beach, CA, USA, Jun. 2019, pp. 7098–7107.
- Y. Zhou, H. Qi, and Y. Ma, “End-to-end wireframe parsing,” in Int. Conf. Comput. Vis., Seoul, Korea (South), Oct. 2019, pp. 962–971.
- Y. Zhou, H. Qi, Y. Zhai, Q. Sun, Z. Chen, L.-Y. Wei, and Y. Ma, “Learning to reconstruct 3d manhattan wireframes from a single image,” in Int. Conf. Comput. Vis., Seoul, Korea (South), Oct. 2019, pp. 7697–7706.
- N. Xue, T. Wu, S. Bai, F. Wang, G.-S. Xia, L. Zhang, and P. H. Torr, “Holistically-attracted wireframe parsing,” in IEEE Conf. Comput. Vis. Pattern Recog., Seattle, WA, USA, Jun. 2020, pp. 2785–2794.
- N. Xue, T. Wu, S. Bai, F.-D. Wang, G.-S. Xia, L. Zhang, and P. H. S. Torr, “Holistically-attracted wireframe parsing: From supervised to self-supervised learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 12, pp. 14 727–14 744, 2023.
- N. Kong, K. Park, and H. Goka, “Hole-robust wireframe detection,” in IEEE Winter Conf. Appl. Comput. Vis., Waikoloa, HI, USA, Jan. 2022, pp. 2684–2693.
- D. Gillsjö, G. Flood, and K. Åström, “Semantic room wireframe detection from a single view,” in Int. Conf. Pattern Recog., Montreal, QC, Canada, Aug. 2022, pp. 1886–1893.
- E. J. Almazàn, R. Tal, Y. Qian, and J. H. Elder, “Mcmlsd: A dynamic programming approach to line segment detection,” in IEEE Conf. Comput. Vis. Pattern Recog., Honolulu, HI, USA, Jul. 2017, pp. 5854–5862.
- Y. Lin, S. L. Pintea, and J. C. van Gemert, “Deep hough-transform line priors,” in Eur. Conf. Comput. Vis., Glasgow, UK, Aug. 2020, pp. 323–340.
- L. Teplyakov, K. Kaymakov, E. Shvets, and D. Nikolaev, “Line detection via a lightweight cnn with a hough layer,” in Thirteenth International Conference on Machine Vision, Rome, Italy, Jan. 2021, p. 116051B.
- L. Teplyakov, L. Erlygin, and E. Shvets, “Lsdnet: Trainable modification of lsd algorithm for real-time line segment detection,” IEEE Access, vol. 10, pp. 45 256–45 265, 2022.
- R. Pautrat, D. Barath, V. Larsson, M. R. Oswald, and M. Pollefeys, “Deeplsd: Line segment detection and refinement with deep image gradients,” in IEEE Conf. Comput. Vis. Pattern Recog., Vancouver, Canada, Jun. 2023.
- Z. Wang, H. Liu, and F. Wu, “Hld: A robust descriptor for line matching,” in IEEE Int. Conf. Comput.-Aided Des. Comput. Graph., Huangshan, China, Aug. 2009, pp. 128–133.
- Z. Wang, F. Wu, and Z. Hu, “Msld: A robust descriptor for line matching,” Pattern Recognit., vol. 42, no. 5, pp. 941–953, 2009.
- H.-M. Liu, Z.-H. Wang, and C. Deng, “Extend point descriptors for line, curve and region matching,” in Int. Conf. Mach. Learn. Cybern., Qingdao, China, Jul. 2010, pp. 214–219.
- X. G. Wang and J. Su, “A novel descriptor for line (curve) matching,” pp. 92–97, Feb. 2011.
- K. Hirose and H. Saito, “Fast line description for line-based slam,” in Brit. Mach. Vis. Conf., Surrey, Sep. 2012, pp. 83.1–83.11.
- A. O. Ok, J. D. Wegner, C. Heipke, F. Rottensteiner, U. Soergel, and V. Toprak, “Matching of straight line segments from aerial stereo images of urban areas,” ISPRS J. Photogramm. Remote Sens., vol. 74, pp. 133–152, 2012.
- L. Zhang and R. Koch, “Line matching using appearance similarities and geometric constraints.” in Pattern Recognit., Graz, Austria, Aug. 2012, pp. 236–245.
- L. Zhang and R. Koch, “An efficient and robust line segment matching approach based on lbd descriptor and pairwise geometric consistency,” J. Vis. Commun. Image Represent., vol. 24, no. 7, pp. 794–805, 2013.
- B. Verhagen, R. Timofte, and L. Van Gool, “Scale-invariant line descriptors for wide baseline matching,” in IEEE Winter Conf. Appl. Comput. Vis., Steamboat Springs, CO, USA, Mar. 2014, pp. 493–500.
- J. Xing, Z. Wei, and G. Zhang, “A robust line matching method based on local appearance descriptor and neighboring geometric attributes,” in International Symposium on Optoelectronic Technology and Application, Beijing, China, 2016.
- C. Lyu and J. Jiang, “Remote sensing image registration with line segments and their intersections,” Remote Sens., vol. 9, no. 5, 2017.
- W. Cai, J. Cheng, J. Deng, Y. Zhou, H. Xiao, J. Zhang, and K. Luo, “Line segment matching fusing local gradient order and non-local structure information,” Appl. Sci., vol. 12, no. 1, 2022.
- C. Wang, X. R. Shen, and L. Ji, “A line descriptor for slam (ldfs),” in Advances in Guidance, Navigation and Control, Tianjin, China, Oct. 2022, pp. 4581–4593.
- X. Lin, Y. Zhou, Y. Liu, and C. Zhu, “Illumination-insensitive line binary descriptor based on hierarchical band difference,” IEEE Trans. Circuits Syst. II, 2023.
- H. Bay, V. Ferraris, and L. Van Gool, “Wide-baseline stereo matching with line segments,” in IEEE Conf. Comput. Vis. Pattern Recog., San Diego, CA, USA, Jun. 2005, pp. 329–336 vol. 1.
- J. López, R. Santos, X. R. Fdez-Vidal, and X. M. Pardo, “Two-view line matching algorithm based on context and appearance in low-textured images,” Pattern Recognit., vol. 48, no. 7, pp. 2164–2184, 2015.
- J. Xing, Z. Wei, and G. Zhang, “A line matching method based on multiple intensity ordering with uniformly spaced sampling,” Sensors, vol. 20, no. 6, 2020.
- B. Huet and E. Hancock, “Line pattern retrieval using relational histograms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 12, pp. 1363–1370, 1999.
- L. Wang, U. Neumann, and S. You, “Wide-baseline image matching using line signatures,” in Int. Conf. Comput. Vis., Kyoto, Japan, Sep. 2009, pp. 1311–1318.
- M. Chen and Z. Shao, “Robust affine-invariant line matching for high resolution remote sensing images,” Photogramm. Eng. Remote Sensing, vol. 79, no. 8, pp. 753–760, 2013.
- G. Yammine, E. Wige, F. Simmet, D. Niederkorn, and A. Kaup, “A novel similarity-invariant line descriptor for geometric map registration,” in IEEE Int. Conf. Image Process., Melbourne, VIC, Australia, Sep. 2013, pp. 3017–3021.
- G. Yammine, E. Wige, F. Simmet, D. Niederkorn, and A. Kaup, “Novel similarity-invariant line descriptor and matching algorithm for global motion estimation,” IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 8, pp. 1323–1335, 2014.
- X. Shi and J. Jiang, “Automatic registration method for optical remote sensing images with large background variations using line segments,” Remote Sens., vol. 8, no. 5, 2016.
- Y. Li and R. L. Stevenson, “Multimodal image registration with line segments by selective search,” IEEE Trans. Cybern., vol. 47, no. 5, pp. 1285–1298, 2017.
- H. Kim and S. Lee, “A novel line matching method based on intersection context,” in IEEE Int. Conf. Robot. Autom., Anchorage, AK, USA, May 2010, pp. 1014–1021.
- H. Kim and S. Lee, “Simultaneous line matching and epipolar geometry estimation based on the intersection context of coplanar line pairs,” Pattern Recognit. Lett., vol. 33, no. 10, pp. 1349–1363, 2012.
- M. Al-Shahri and A. Yilmaz, “Line matching in wide-baseline stereo: A top-down approach,” IEEE Trans. Image Process., vol. 23, no. 9, pp. 4199–4210, 2014.
- H. Kim, S. Lee, and Y. Lee, “Wide-baseline stereo matching based on the line intersection context for real-time workspace modeling,” J. Opt. Soc. Am. A Opt. Image Sci. Vis., vol. 31, no. 2, pp. 421–435, 2014.
- K. Li, J. Yao, and X. Lu, “Robust line matching based on ray-point-ray structure descriptor,” in Asian Conference on Computer Vision, Singapore, Nov. 2015, pp. 554–569.
- K. Li, J. Yao, X. Lu, L. Li, and Z. Zhang, “Hierarchical line matching based on line-junction-line structure descriptor and local homography estimation,” Neurocomputing, vol. 184, no. C, p. 207–220, 2016.
- K. Li and J. Yao, “Line segment matching and reconstruction via exploiting coplanar cues,” ISPRS J. Photogramm. Remote Sens., vol. 125, pp. 33–49, 2017.
- S. Zhuang, D. Zou, L. Pei, and D. H. P. Liu, “A binary robust line descriptor,” in International Conference on Indoor Positioning Indoor Navigation, Sapporo, Japan, Sep. 2016.
- M. Lange, F. Schweinfurth, and A. Schilling, “Dld: A deep learning based line descriptor for line feature matching,” in IEEE Int. Conf. Intell. Robots Syst., Macau, China, Nov. 2019, pp. 5910–5915.
- M. Lange, C. Raisch, and A. Schilling, “Wld: A wavelet and learning based line descriptor for line feature matching,” in Vis., Model., Vis., Tübingen, Germany, Sep. 2020.
- H. Liu, Y. Liu, M. Fu, Y. Wei, Z. Huo, and Y. Qiao, “Towards learning line descriptors from patches: a new paradigm and large-scale dataset,” Int. J. Mach. Learn. Cybern., vol. 12, no. 3, pp. 877–890, 2021.
- A. Vakhitov and V. Lempitsky, “Learnable line segment descriptor for visual slam,” IEEE Access, vol. 7, pp. 39 923–39 934, 2019.
- S. Yoon and A. Kim, “Line as a visual sentence: Context-aware line descriptor for visual localization,” IEEE Robot. Autom. Lett., vol. 6, no. 4, pp. 8726–8733, 2021.
- X. Cao, Y. Huang, Y. Huang, Y. Li, and S. Cai, “Ldam: line descriptors augmented by attention mechanism,” in International Conference on Digital Image Processing, Wuhan, China, Oct. 2022, p. 1234203.
- P. Denis, J. H. Elder, and F. J. Estrada, “Efficient edge-based methods for estimating manhattan frames in urban imagery,” in Eur. Conf. Comput. Vis., Marseille, France, Oct. 2008, pp. 197–210.
- K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1615–1630, 2005.
- V. Balntas, K. Lenc, A. Vedaldi, T. Tuytelaars, J. Matas, and K. Mikolajczyk, “ℍℍ\mathbb{H}blackboard_Hh-patches: A benchmark and evaluation of handcrafted and learned local descriptors,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 11, pp. 2825–2841, 2020.
- H. Lin, V. Hosu, and D. Saupe, “Kadid-10k: A large-scale artificially distorted iqa database,” in Int. Conf. Qual. Multimed. Exp., QoMEX, no. 05-07 Jun., Berlin, Germany, 2019, pp. 1–3.
- H. Zhou, T. Sattler, and D. W. Jacobs, “Evaluating local features for day-night matching,” in Eur. Conf. Comput. Vis., Amsterdam, The Netherlands, Oct. 2016, pp. 724–736.
- R. Pautrat, V. Larsson, M. R. Oswald, and M. Pollefeys, “Online invariance selection for local feature descriptors,” in Eur. Conf. Comput. Vis., Glasgow, UK, Aug. 2020, pp. 707–724.
- P. Mukhopadhyay and B. B. Chaudhuri, “A survey of hough transform,” Pattern Recognit., vol. 48, no. 3, pp. 993–1010, 2015.
- P. S. Rahmdel, R. Comley, D. Shi, and S. McElduff, “A review of hough transform and line segment detection approaches,” in Int. Conf. Comput. Vis. Theory Appl., Berlin, Germany, Dec. 2015.
- P. Nacken, “A metric for line segments,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 12, pp. 1312–1318, 1993.
- N. Kiryati and A. Bruckstein, “What’s in a set of points? (straight line fitting),” IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 4, pp. 496–500, 1992.
- M. Linderbaum and A. Bruckstein, “On recursive, o(n) partitioning of a digitized curve into digital straight segments,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 9, pp. 949–953, 1993.
- H. Germain, G. Bourmaud, and V. Lepetit, “S2dnet: Learning image features for accurate sparse-to-dense matching,” in Eur. Conf. Comput. Vis., Glasgow, United Kingdom, Aug. 2020, p. 626–643.
- L. Porzi, S. Rota Bulò, and E. Ricci, “A deeply-supervised deconvolutional network for horizon line detection,” in Proc. ACM Multimed. Conf., Amsterdam, The Netherlands, Oct. 2016.
- G. Simon, A. Fond, and M.-O. Berger, “A-contrario horizon-first vanishing point detection using second-order grouping laws,” in Eur. Conf. Comput. Vis., Munich, Germany, Sep. 2018.
- L. Duan and F. Lafarge, “Image partitioning into convex polygons,” in IEEE Conf. Comput. Vis. Pattern Recog., Boston, MA, USA, Jun. 2015, pp. 3119–3127.
- Y. Zhao and S.-C. Zhu, “Scene parsing by integrating function, geometry and appearance models,” in IEEE Conf. Comput. Vis. Pattern Recog., Portland, OR, USA, Jun. 2013, pp. 3119–3126.
- X. Lin, C. Zhu, Q. Zhang, X. Huang, and Y. Liu, “Efficient and robust corner detectors based on second-order difference of contour,” IEEE Signal Process. Lett., vol. 24, no. 9, pp. 1393–1397, 2017.