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 |
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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.
Original laser stripe image and processing image. (a) Original laser stripe image; (b) Result of bilateral filter; (c) Result of Gaussian filter
Comparison of different threshold segmentation methods. (a) Image binary based on extreme value threshold; (b) Image binary based on OTSU; (c) Image binary based on double threshold; (d) Result of laser stripe center extraction
Laser stripe image captured by different conditions. (a) Laser tripe image under bright condition; (b) Laser tripe image under dark condition
Comparison of residuals under bright condition
Comparison of residuals under dark condition