Abstract
In this paper, a calibration method for structured light system is proposed, which is based on pseudo-random coding theory to generate binary shape-coded pattern. In this method, the checkerboard and binary shape-coded patterns are captured by the camera of the structured light system method. Based on the geometric feature of binary shape-coded pattern, a feature point detector is designed. Then, the feature points in the binary geometric image are extracted, and the topological structure is constructed. After that, the pattern elements are extracted with the affine transformation theory and bilinear interpolation algorithm. The identification of pattern elements is modeled as a supervised classification problem, and the convolutional neural network technique is adopted to recognize the pattern elements by collecting a large number of training samples. Thus, the code-words of the feature points are confirmed. According to the projective transformation principle, the correspondence between the camera image plane and projector image plane is determined. Then, the corner points in the camera image plane are transformed into the projector image plane with the correspondence. Thereby the camera and projector are calibrated with Zhang’s calibration method, and the system calibration is achieved. The experimental results show that the calibration accuracy can reach about 0.2 pixels with the proposed method and the quality of reconstructed surface is great.
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Acknowledgments
This work was supported in part by the National Key Research and Development Program of China (2017YFB1103600), Shenzhen Science Plan (CXZZ20140417113430730, JSGG20150925164740726), Shenzhen Technology Project under Grant (JCYJ20170413152535587), Shenzhen Key Laboratory of Acousto-Optic Detection Technology and Equipment (2015-74), and the National Natural Science Foundation of China (61375041, U1613213).
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Zeng, H., Tang, S., Song, Z., Gu, F., Huang, Z. (2017). Calibration of a Structured Light Measurement System Using Binary Shape Coding. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_53
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DOI: https://doi.org/10.1007/978-3-319-68345-4_53
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