3D reconstruction system for semi-automatic estimation of objects length and area by means of stereo vision

  • Bryan García Institución Universitaria Antonio José Camacho, Cali Universidad del Valle, Cali
  • Carlos Diego Ferrin B. Institución Universitaria Antonio José Camacho, Cali
  • Jorge Humberto Erazo Institución Universitaria Antonio José Camacho, Cali Universidad del Valle, Cali
Keywords: Stereo vision, 3D reconstruction, digital image processing.

Abstract

It is mandatory to characterize dimensionally the manufactured industrial pieces for quality control purposes. As it is not possible to touch some pieces when trying to retrieve dimensional information, then non-invasive techniques are required to do so. Stereo vision is a passive technology which is both robust and accurate for non-invasive applications. For this reason, in this work we describe the design and implementation of a 3D reconstruction system for the estimation of the length and area of certain objects. This tool allows to easily incorporate new image correspondence techniques to its main execution pipeline. We carry some experiments and show certain benefits when selecting an accurate image correspondence technique for the estimation of the length and area.

Author Biographies

Bryan García, Institución Universitaria Antonio José Camacho, Cali Universidad del Valle, Cali

Electronic Engineer (2016) and student of the Master in Engineering with emphasis in Automation of the Universidad del Valle (Cali, Colombia). His interest areas are: artificial vision, thermography, machine learning, and signals/images processing

Carlos Diego Ferrin B., Institución Universitaria Antonio José Camacho, Cali

Physics Engineer (Universidad del Cauca, Popayan – Colombia); Master in Electronics Engineering (Universidad del Valle, 2015) and student of the Doctorate in Engineering with emphasis in Electrics and Electronics of the Universidad del Valle. He was beneficiary of the “young researchers” program of Colciencias (2011) and his interest areas are: artificial vision, machine learning, and processing of signals, images, and point clouds

Jorge Humberto Erazo, Institución Universitaria Antonio José Camacho, Cali Universidad del Valle, Cali

Electronic Engineer (2014), Master in Engineering with emphasis in Electronics (2010) and student of the Doctorate in Engineering with emphasis in Electrics and Electronics of the Universidad del Valle. Full time and auxiliary professor affiliated to the engineering faculty of the Institución Universitaria Antonio José Camacho (Cali-Colombia). Thermography professional level I and II of the Infrared Training Center – ITC (2007 and 2011). His areas of interest are: thermography, artificial vision, digital signal processing, and pattern recognition

References

Bodkin, B. H. (2012). Real-Time Mobile Stereo Vision. University of Tennessee. Retrieved from http://trace.tennessee.edu/utk_gradthes/1313

Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., & Ranzuglia, G. (2008). MeshLab: an Open-Source Mesh Processing Tool. Sixth Eurographics Italian Chapter Conference, (pp. 129-136). doi:10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2008/129-136

De Loera, J. A., Rambau, J., & Santos, F. (2010). Triangulations (Vol. 25). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-12971-1

Demant, C., Streicher-Abel, B., & Garnica, C. (2013). Industrial image processing. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-33905-9

Ferrin, C. (2017). UNIAJC: Proyecto visión estéreo. Retrieved from https://github.com/cdfbdex/UNIAJC_VISION-ESTEREO

Foix, S. & Aleny, G. (2011). Lock-in time-of-flight (ToF) cameras: A survey. IEEE Sensors Journal, 11(3). doi: 10.1109/JSEN.2010.2101060

Hagen, H., Disch, A., Ehret, J., Klein, R., Sascha, K., Zeckzer, D., & Michael, M. (n.d.). Visual inspection methods for quality control in automotive engineering. Visualization, 2004 IEEE. doi:10.1109/VISUAL.2004.111

Hamzah, R. A., Hamid, A. M. A., & Salim, S. I. M. (2010). The solution of stereo correspondence problem using block matching algorithm in stereo vision mobile robot. 2010 Second International Conference on Computer Research and Development, (pp. 733-737). doi:10.1109/ICCRD.2010.167

Hamzah, R. A. & Ibrahim, H. (2016). Literature survey on stereo vision disparity map algorithms. Journal of Sensors, 2016. Art. 8742920. doi:10.1155/2016/8742920

Hansard, M., Lee, S., Choi, O., & Horaud, R. (2013). Time of flight cameras: Principles, methods, and applications. London, UK: Springer. https://doi.org/10.1007/978-1-4471-4658-2

Kim, M. Y., Ayaz, S. M., Park, J., & Roh, Y. (2014). Adaptive 3D sensing system based on variable magnification using stereo vision and structured light. Optics and Lasers in Engineering, 55, 113-127. doi:10.1016/j.optlaseng.2013.10.021

Krig, S. (2014). Computer Vision Metrics. Computer Vision Metrics: Survey, Taxonomy, and Analysis. Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4302-5930-5

Lee, S., Lee, J. H., Lim, J., & Suh, I. H. (2015). Robust stereo matching using adaptive random walk with restart algorithm. Image and Vision Computing, 37, 1-11. doi:10.1016/j.imavis.2015.01.003

Szeliski, R. (2011). Computer vision. Media (Vol. 42). London, UK: Springer. doi:10.1007/978-1-84882-935-0

Wöhler, C. (2013). 3D Computer vision. London, UK: Springer. doi:10.1007/978-1-4471-4150-1

Zhang, S., Wang, C., & Chan, S. C. (2013). A new high resolution depth map estimation system using stereo vision and kinect depth sensing. Journal of Signal Processing Systems, 79(1), 19-31. doi:10.1007/s11265-013-0821-8
Published
2017-04-05
Section
Original Research