The stereoscopic analysis and depth map creation

  • Ondřej Švehla
Keywords: Stereoscopic image, Depth map, EmguCV, Basler

Abstract

This contribution is focused on the use of the stereoscopic image for the purpose of depth map creation. Further, methods for calibration of the camera(s) are discussed. A stereoscopic head was constructed for the purpose creating stereoscopic image. Two industrial cameras Basler acA1600-20uc with lens Computar M2514-MP2 were used for constructing this head. Furthermore, the algorithm for obtaining the depth map is described. A programing language C# and EmguCV library were used to the implementation of algorithm. The algorithm consists of 4 parts. The calibration of the camera(s) and image acquisition is solved as first. Calibration of the camera(s) is solved by detection of intersections on the chessboard. Further, methods for the purpose of depth map obtaining are described. The implemented algorithm is tested in the end. 

References

Bennett, S. and Lasenby, J. 2014. ChESS – Quick and Robust Detection of Chess-Board Features. Computer Vision and Image Understanding, 118, 197–210.

Cao, X. and Foroosh, H. 2006. Camera Calibration Using Symmetric Objects. IEEE Transactions on Image Processing, 15 (11), 3614–3619.

Chen, G., Guo, Y., Wang, H., Ye, D. and Gu, Y. 2012a. Stereo Vision Sensor Calibration Based on Random Spatial Points Given by CMM. Optik – International Journal for Light and Electron Optics, 123 (8), 731–734.

Chen, J., Benzeroual, K. and Allison, R. S. 2012b. Calibration for High-Definition Camera Rigs with Marker Chessboard. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 29–36.

Chu, J., GuoLu, A. and Wang, L. 2013. Chessboard Corner Detection under Image Physical Coordinate. Optics & Laser Technology, 48, 599–605.

de la Escalera, A. and Armingol, J. M. 2010. Automatic Chessboard Detection for Intrinsic and Extrinsic Camera Parameter Calibration. Sensors, 10 (3), 2027–2044.

Devy, M., Garric, V. and Orteu, J. J. 1997. Camera Calibration from Multiple Views of a 2D Object, Using a Global Nonlinear Minimization Method. In: Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems, pp. 1583–1589.

Dröppelmann, S., Hueting, M., Latour, S. and van der Veen, M. 2010. Stereo Vision using the OpenCV Library. [online]. Available at: https://pdfs.semanticscholar.org/8149/5e6c1e6c3460b14f4be8e7876f1d6659f5c1.pdf. [Accessed 2016, December 20].

Emgu CV. 2012. Stereo Imaging. [online]. Available at: http://www.emgu.com/wiki/index.php/Stereo_Imaging. [Accessed 2016, December 8].

Fathi, H. and Brilakis, I. 2016. Multistep Explicit Stereo Camera Calibration Approach to Improve Euclidean Accuracy of Large-Scale 3D Reconstruction. Journal of Computing in Civil Engineering, 30 (1).

George, M. A. and George, A. M. 2014. Stereovision for 3D Information. In: Babu, B. V. et al. (eds.). Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), pp. 1595–1602.

Gu, W., Yin, J., Yang, X. F. and Liu, P. 2014. Disparity Map Acquisition Based on Matlab Calibration Toolbox and OpenCV Stereo Matching Algorithm. Advanced Materials Research, 926–930, 3030–3033.

Kamencay, P., Brezňan, M., Jarina, R., Lukáč, P. and Zachariášová, M. 2012. Improved Depth Map Estimation from Stereo Images based on Hybrid Method. Radioengineering, 21 (1), 70–78.

Kolomazník, J., Ondroušek, V. and Vytečka, M. 2013. Stereoscopic Analysis of the Technological Scene. In: 19th International Conference on Soft Computing MENDEL 2013. Brno: Vysoké učení technické v Brně, pp. 353–356. ISBN 978-80-214-4755-4.

Laureano, G. T., de Paiva, M. S. V., da Silva Soares, A. and Coelho, C. J. 2015. A Topological Approach for Detection of Chessboard Patterns for Camera Calibration. Emerging Trends in Image Processing, Computer Vision and Pattern Recognition, chapter 34, pp. 517–531.

Lindner, M., Schiller, I., Kolb, A. and Koch, R. 2010. Time-of-Flight Sensor Calibration for Accurate Range Sensing. Computer Vision and Image Understanding, 114 (12), 1318–1328.

National Instruments. 2016. 3D Imaging with NI LabVIEW. [online]. Available at: http://www.ni.com/white-paper/14103/en/. [Accessed 2016, April 10].

Placht, S., Fürsattel, P., Mengue, E. A., Hofmann, H., Schaller, C., Balda, M. and Angelopoulou, E. 2014. ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration. In: Fleet, D. et al. (eds.). ECCV 2014, Part IV, pp. 766–779.

Prokos, A., Kalisperakis, I., Petsa, E. and Karras, G. 2012. Automatic Calibration of Stereo-Cameras Using Ordinary Chess-Board Patterns. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B5, 45–49.

Rambhia, J. 2013. Disparity Map. [online]. Available at: http://www.jayrambhia.com/blog/disparity-mpas. [Accessed 2016, October 31].

Revuelta Sanz, P., Ruiz Mezcua, B., Sánchez Pena, J. M. and Thiran, J.-P. 2011. Stereo Vision Matching over Single-Channel Color-Based Segmentation. In: Proceedings of the International Conference on Signal Processing and Multimedia Applications (SIGMAP), pp. 126–130.

Sun, J., Liu, Q., Liu, Z. and Zhang, G. 2011. A Calibration Method for Stereo Vision Sensor with Large FOV based on 1D Targets. Optics and Lasers in Engineering, 49 (11), 1245–1250.

Wang, F., Jia, K. and Feng, J. 2017. The Real-Time Depth Map Obtainment Based on Stereo Matching. In: Pan, J. et al. (eds.). Intelligent Data Analysis and Applications, pp. 138–144.
Published
2016-12-30
Section
Articles