Implementation of the smartphone camera in the measures of narrow street facades
Abstract
This paper aims to perform metric measurements of narrow street façades using single image captured by smartphone’s camera. Since tight area accompanied by narrow street limits object to camera distance, object lines perpendicular to façade do not appear in image and consequently their vanishing point (VP) is hard to detect. Accordingly, semi-automated MATLAB® application was designed depending only on two orthogonal VPs. Novelty of work comes from using smartphone as a cost and time efficient tool for measurements, depending only on two VPs, and applying image line refinement approach exploiting detected VPs. Three single images were captured by three different smartphones. Then, undistorted single images were formed after calibrating cameras. Image lines for horizontal and vertical object lines were extracted semi-automatically. Two VPs were detected applying two models: Model-I solves for vanishing points’ Cartesian coordinates, whereas Model-II solves for angle coordinate peaks of histogram. Image line refinement approach was applied before applying cross-ratio using one horizontal and one vertical reference lines to calculate object lengths of 46 check lines (horizontal and vertical). Proposed models provided reliable and comparable results. Applying line refinement approach improved solution with best overall accuracy of 0.010 m and 0.011 m for Model-I and Model-II, respectively.
Keyword : single image, metric measurements, façade observation, vanishing point, cross-ratio, smartphone’s camera, semi-automated line extraction
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aldelgawy, M., & Abu-Qasmieh, I. (2021a). Calibration of smartphone’s rear dual camera system. Geodesy & Cartography, 47(4), 162–169. https://doi.org/10.3846/gac.2021.13434
Aldelgawy, M., & Abu-Qasmieh, I. (2021b). Semi-automatic reconstruction of object lines using a smartphone’s dual camera. The Photogrammetric Record, 36(176), 381–401. https://doi.org/10.1111/phor.12388
Arslan, O. (2018). Accuracy assessment of single viewing techniques for metric measurements on single images. IET Computer Vision, 12(5), 693–701. https://doi.org/10.1049/iet-cvi.2017.0549
Burger, W. (2019). Zhang’s camera calibration algorithm: In-depth tutorial and implementation (Technical Report HGB16-05). Department of Digital Media, University of Applied Sciences Upper Austria, School of Informatics, Communications and Media.
Burger, W., & Burge, M. J. (2016). Digital image processing: An algorithmic introduction using Java (2nd ed.). Springer. https://doi.org/10.1007/978-1-4471-6684-9
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679–698. https://doi.org/10.1109/TPAMI.1986.4767851
Caprile, B., & Torre, V. (1990). Using vanishing points for camera calibration. The International Journal of Computer Vision, 4(2), 127–140. https://doi.org/10.1007/BF00127813
Chuang, J.-H., Kao, J.-H., Lin, H.-H., & Chiu, Y.-T. (2007). Practical error analysis of cross-ratio-based planar localization. In Pacific-Rim Symposium on Image and Video Technology (pp. 727–736). Springer. https://doi.org/10.1007/978-3-540-77129-6_62
Debevec, P., Taylor, C., & Malik, J. (1996). Modeling and rendering architecture from photographs: A hybrid geometry- and image-based approach. In Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques (pp. 11–20). https://doi.org/10.1145/237170.237191
Di, H., Wang, L., & Xu, G. (2003). A three-step technique of robust line detection with modified Hough transform. In Proceedings of the SPIE (Vol. 5286, pp. 835–838). https://doi.org/10.1117/12.538699
Erdnüß, B. (2018). Measuring in images with projective geometry. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. XLII-1, pp. 141–148). https://doi.org/10.5194/isprs-archives-XLII-1-141-2018
Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395. https://doi.org/10.1145/358669.358692
Hassanein, A., Mohammad, S., Sameer, M., & Ragab, M. (2015). A survey on hough transform, theory, techniques and applications. International Journal of Computer Science Issues, 12(1), 139–156.
Hough, P. (1962). Method and means for recognizing complex (US Patent 3,069,654, Ser. No. 17,7156 Claims). https://patents.google.com/patent/US3069654A/en
Lee, S. C., & Nevatia, R. (2003). Interactive 3D building modeling using a hierarchical representation. In First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis (pp. 58–65). IEEE.
Li, B., Peng, K., Ying, X., & Zha, H. (2010). Simultaneous vanishing point detection and camera calibration from single images. In International Symposium on Visual Computing (pp. 151–160). Springer. https://doi.org/10.1007/978-3-642-17274-8_15
Liu, J.-S., & Chuang, J.-H. (2002). A geometry-based error estimation for cross-ratios. Pattern Recognition, 35(1), 155–167. https://doi.org/10.1016/S0031-3203(00)00174-6
Mikhail, E. M., & Ackermann, F. E. (1982). Observations and least squares (2nd ed.). University Press of America.
Oh, S. H., & Jung, S. K. (2012). Ransac-based or thogonal vanishing point estimation in the equirectangular images. Journal of Korea Multimedia Society, 15(12), 1430–1441. https://doi.org/10.9717/kmms.2012.15.12.1430
Rong, W., Li, Z., Zhang, W., & Sun, L. (2014). An improved Canny edge detection algorithm. In 2014 IEEE International Conference on Mechatronics and Automation (pp. 577–582), Tianjin, China. https://doi.org/10.1109/ICMA.2014.6885761
Vouzounaras, G., Daras, P., & Strintzis, M. (2014). Automatic generation of 3D outdoor and indoor building scenes from a single image. Multimedia Tools and Applications, 70(1), 361–378. https://doi.org/10.1007/s11042-011-0823-0
Wang, J., Shi, F., Zhang, J., & Liu, Y. (2006). A new calibration model and method of camera lens distortion. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 5713–5718), Beijing, China. https://doi.org/10.1109/IROS.2006.282376
Wang, M., Liu, X., Zhen, Y., & Lu, Y. (2010). Plane geometric information extraction from single image based on cross ratio. 18th International Conference on Geoinformatics (pp. 1–5), Beijing, China. https://doi.org/10.1109/GEOINFORMATICS.2010.5567625
Wildenauer, H., & Hanbury, A. (2012). Robust camera self-calibration from monocular images of Manhattan worlds. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 2831–2838). IEEE. https://doi.org/10.1109/CVPR.2012.6248008
Wong, T., Tao, C., Cheng, Y., Wong, K., & Tam, C. (2014). Application of cross-ratio in traffic accident reconstruction. Forensic Science International, 235, 19–23. https://doi.org/10.1016/j.forsciint.2013.11.012
Zhang, G., He, J., & Yang, X. (2003). Calibrating camera radial distortion with cross-ratio invariability. Optics & Laser Technology, 35(6), 457–461. https://doi.org/10.1016/S0030-3992(03)00053-7
Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis & Machine Intelligence, 22(11), 1330–1334. https://doi.org/10.1109/34.888718