Abstract

In the last 10 years, industrial image processing has been used in many areas such as automotive, medicine and aviation. Especially for sensitive measurement applications, camera control systems are preferred due to their speed and quality advantages. In this study, a method is proposed to determine the optimum camera position at the image acquisition phase for industrial cameras. In the proposed approach, images are captured while the camera and telecentric lens, which are connected to the servo motor, are moving up and down. The edge informations of the captured images are extracted in the x and y directions and the amplitude value is calculated. Then, meaningful and meaningless edge information at each motor position is found with a median-based approach. Finally, the sharpness of the images are arranged by statistical analysis and the optimum focal point for the camera is found. Sobel, Robert and Prewitt Filters were applied to determine the best method for extracting edge information. Measurements were performed on the workpieces using camera positions where the focus was optimum for each method. According to the results, it has been observed that the camera position, which produces the closest result to the real values, is obtained from the focus determined by the Sobel Filter. The proposed method is leaded to researchers to provide automatic focus for camera control systems.

References

[1] Krotkov, Eric. "Focusing." International Journal of Computer Vision 1.3 (1988): 223-237.

[2] Nayar, Shree K., and Yasuo Nakagawa. "Shape from focus." IEEE Transactions on Pattern analysis and machine intelligence 16.8 (1994): 824-831. 

[3] Zhang, Yani, Ying Zhang, and Changyun Wen. "A new focus measure method using moments." Image and Vision computing 18.12 (2000): 959-965. 

[4] Chen, Chih-Yung, Rey-Chue Hwang, and Yu-Ju Chen. "A passive auto-focus camera control system." Applied soft computing 10.1 (2010): 296-303.

[5] Li, Dong, and Jindong Tian. "An accurate calibration method for a camera with telecentric lenses." Optics and Lasers in Engineering 51.5 (2013): 538-541.

[6] Pan, Bing, Liping Yu, and Dafang Wu. "High-accuracy 2D digital image correlation measurements using low-cost imaging lenses: implementation of a generalized compensation method." Measurement Science and Technology 25.2 (2013): 025001.

[7] Israni, Dippal, Sandip Patel, and Arpita Shah. "Comparison of different techniques of camera autofocusing." Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1. Springer, Cham, 2016.

[8] Shih, Loren. "Autofocus survey: a comparison of algorithms." Digital photography III. Vol. 6502. SPIE, 2007. 

[9] Xu, Xin, et al. "Robust passive autofocus system for mobile phone camera applications." Computers & Electrical Engineering 40.4 (2014): 1353-1362.

[10] Wang, Leyuan, et al. "An End-to-End Autofocus Camera for Iris on the Move." 2021 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2021.

[11] Fricke, Dierk, et al. "Non-Contact Dermatoscope with Ultra-Bright Light Source and Liquid Lens-Based Autofocus Function." Applied Sciences 9.11 (2019): 2177.

[12] Subbarao, Murali, and JennKwei Tyan. "Optimal focus measure for passive autofocusing and depth-from-focus." Videometrics IV. Vol. 2598. SPIE, 1995.

[13] Bucher, Izhak. "Circle fit." Matlab central file exchange (2004).