The Performance Evaluation of Two Different Distance Estimation Tools Under Unclean Water Using Stereo Vision
[1]
Shadi Mahmoodi Khaniabadi, School of Electrical and Electronic, University Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia.
[2]
Ali Khalili Mobarakeh, Department of Mechanical Engineering, University of Málaga, Doctor Ortiz Ramos, Malaga, Spain.
[3]
Saba Nazari, School of Electrical and Electronic, University Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia.
[4]
Abolfazl Zargari, Department of Electrical and computer Engineering, University of Oklahoma, Norman, United States.
Stereo vision is one of the best methods for distance estimation of underwater object. In this research two pairs of cameras were used as stereo image acquisition to estimate the distance of underwater object. The stereo vision system in this project consists of calibration of camera, rectification of images, segmentation of images, finding of centroid and localization of object. Edge-based segmentation, Mathematical morphology and largest area selection are used to perform image segmentation. As a result, it is proved that the curve fitting tools method is more dependable than triangulation method to evaluate the coordinates. The final experiment results illustrate that the overall error of curve fitting tools method in the unclean (muddy) water conditions is 0.2 cm, while by using triangulation in the same condition is around 1.5cm.
Stereo Vision, Under Water Range Estimation, Image Segmentation, Curve Fitting Tool
[1]
Tauscher, S., et al., OpenIGTLink interface for state control and visualisation of a robot for image-guided therapy systems. Int J Comput Assist Radiol Surg, 2015. 10 (3): p. 285-92.
[2]
Chyi-Yeu, L. and C. Yi-Pin. The DSP based catcher robot system with stereo vision. in 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. 2008.
[3]
Milan Sonka, V. H., Roger Boyle, Image Processing, Analysis and Machine Vision. 1993: Springer US.
[4]
Hema, C. R., et al. Object Localization using Stereo Sensors for Adept SCARA Robot. in 2006 IEEE Conference on Robotics, Automation and Mechatronics. 2006.
[5]
Eng Swee, K., A. H. A. Hassan, and A. Ranjbaran. Stereo vision with 3D coordinates for robot arm application guide. in 2010 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology. 2010.
[6]
Sbnchez, A. J. and J. M. Martinez. Robot-arm pick and place behavior programming system using visual perception. in Proceedings 15th International Conference on Pattern Recognition. ICPR-2000. 2000.
[7]
Nam, K. W., et al., Application of Stereo-Imaging Technology to Medical Field. Healthc Inform Res, 2012. 18 (3): p. 158-63.
[8]
Shadi Mahmoodi Khaniabadi, A. K. M., Saba Nazari, Rana Sabouni Tabari, and A. Z. Azar Golshahi Porshokoh, The Performance Assessment of Curve Fitting Tools of Underwater Object with Glass Condition Using Stereo Vision. International Journal of Computer Science and Control Engineering, 2016. 4 (3): p. 14-17.
[9]
Theodor Borangiu, A. D., Robot Arms with 3D Vision Capabilities. Advances in Robot Manipulators, 2010 (Available from: https://www.intechopen.com/books/advances-in-robot-manipulators/robot-arms-with-3d-vision-capabilities).
[10]
Klette, R. Stereo-vision-support for intelligent vehicles-the need for quantified evidence. in Australasian Joint Conference on Artificial Intelligence. 2008. Springer.
[11]
Silpa-Anan, C., Autonomous underwater robot: Vision and control. 2001: Australian National University.
[12]
Albitar, H., et al., Underwater Robotics: Surface Cleaning Technics, Adhesion and Locomotion Systems. International Journal of Advanced Robotic Systems, 2016. 13 (1): p. 7.
[13]
Brown, M. Z., D. Burschka, and G. D. Hager, Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003. 25 (8): p. 993-1008.
[14]
Okutomi, M., Y. Katayama, and S. Oka, A Simple Stereo Algorithm to Recover Precise Object Boundaries and Smooth Surfaces. International Journal of Computer Vision, 2002. 47 (1): p. 261-273.
