Gan Hong, Zhang Chao, Li Lin, et al. Sub-pixel extraction of laser stripe in complex background[J]. Opto-Electronic Engineering, 2019, 46(2): 180457. doi: 10.12086/oee.2019.180457
Citation: Gan Hong, Zhang Chao, Li Lin, et al. Sub-pixel extraction of laser stripe in complex background[J]. Opto-Electronic Engineering, 2019, 46(2): 180457. doi: 10.12086/oee.2019.180457

Sub-pixel extraction of laser stripe in complex background

    Fund Project: Supported by the National Key Research and Development Program of China (2018YFB120181), National Natural Science Foundation Projects (51608123), and Fujian Natural Science Foundation (2017J01682, 2017J01475)
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  • The complex background and laser stripe noise affect laser stripe extraction. Adaptive double threshold segmentation method and the improved gray weight model are proposed in this study. First, the characteristics of the laser stripe and the source of noise in the image are investigated. Bilateral filter is applied to remove the noise of images. Subsequently, the gray histogram of laser image and the double threshold are computed. By sub-regional processing, initial stripe center and stripe width of binary images are obtained. Finally, the sub-pixel center of the laser strip is extracted by the proposed model. The double threshold segmentation method and the improved gray weight model are compared with the traditional algorithms. The results show that the double threshold method is more accuracy in extracting the laser stripe region than the extreme value method and the Otsu method. Comparing with the residual value of sub-pixel center, the improved gray weight model (0.23) has better results than the gray-gravity method (0.71), the extreme value method (0.86), and the Gaussian fitting method (0.86). The algorithms proposed in this study avoid the impacts of complex background and laser stripe noise, increase the accuracy of the laser stripe center positioning and extract the stripe center extraction fast and accurately in complex backgrounds.
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  • Overview: Laser stripe center extraction is the key step of a structured light vision system, which determines the stability, real-time and accuracy of the system. The results of laser center extraction are affected by many factors, such as changing width of the laser stripe, the complex measuring environment, the optical properties of the measured surface, etc. In current studies, there are many methods presented to extract the laser stripe centre. The traditional extremum method and the geometric center method are low precise and insufficient in robustness. Direction template method can effectively eliminate noise, but it is not effective in extracting laser stripes with complex shapes and varying widths. Furthermore, all the gray information in the laser stripe are considered in the gray-gravity method. Although the accuracy of this method is much higher than other traditional methods, it is easily interfered by the noise with high frequency. Gaussian fitting is a popular method to detect the laser line center position, which eliminates the most noise of laser stripe. The Steger algorithm is precise in positioning and robust to noise. However, this method is complicated and cannot process in real-time.

    The improved gray weight model is proposed to make a balance between the robustness, calculation speeds and the intensive computation. First step is reducing the image noise. Secondly, the laser stripe and the complex background environment need to be divided accurately. Finally, a laser centre extraction method is proposed.

    The raw image contains amount of image noise, which affects the accuracy of the measurement of the structured light vision system. Firstly, the bilateral filter is applied to remove noise of raw images. Subsequently, the gray histogram of the laser image and the double threshold are computed. Based on the sub-regional processing, the initial stripe center and the stripe width in binary images are obtained. Then, a smoothing distance algorithm is used to obtain a continuous centre curve. Finally, sub-pixel center of the strip is extracted based on the proposed model. Double threshold segmentation method and the improved gray weight model are compared with traditional algorithms.

    The results show that adaptive double threshold method is more accurate on the laser stripe region extraction than the extreme value method and the Otsu method. Comparing with the residual value of sub-pixel center, the improved gray weight model (0.23) has the best result, and follows by gray-gravity method (0.71), extreme value method (0.86), and Gaussian fitting method (0.86). The algorithms proposed in this study avoid the impacts of complex background and laser stripe noise, increase the accuracy of the laser stripe center positioning and extract the stripe center extraction fast and accurately in complex backgrounds.

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