• Infrared and Laser Engineering
  • Vol. 50, Issue 12, 20210166 (2021)
Guangbao Tian1, Jian Wang1、*, and Bowen Wang2
Author Affiliations
  • 1College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
  • 2High Speed Institute China Aerodynamics Research and Development Center, Mianyang 621000, China
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    DOI: 10.3788/IRLA20210166 Cite this Article
    Guangbao Tian, Jian Wang, Bowen Wang. Monocular camera non-cooperative target extraction and pose detection[J]. Infrared and Laser Engineering, 2021, 50(12): 20210166 Copy Citation Text show less
    Image segmentation and sub-pixel edge positioning process
    Fig. 1. Image segmentation and sub-pixel edge positioning process
    Original equipment image. (a) Actuator; (b) Receiver; (c) Features on the receiver
    Fig. 2. Original equipment image. (a) Actuator; (b) Receiver; (c) Features on the receiver
    [in Chinese]
    Fig. 3. [in Chinese]
    Pictures under different settings. (a) Images with different exposures in the same position; (b) Images with different positions in the same exposure
    Fig. 3. Pictures under different settings. (a) Images with different exposures in the same position; (b) Images with different positions in the same exposure
    Image of threshold segmentation process after classification. (a) Original image; (b) Image after clustering; (c) Results of segmentation of class 1 and class 3 pixel values; (d) Results of segmentation of class 1 and class 2 pixel values; (e) Results of class 2 and class 3 pixel value segmentation; (f) The result of XOR of figure (d) and figure (e); (g) Results of figure (c), figure (d), figure (e) or operation; (h) The target obtained by removing the pseudo-connected components according to the area relationship of the connected components after inverting figure (g)
    Fig. 4. Image of threshold segmentation process after classification. (a) Original image; (b) Image after clustering; (c) Results of segmentation of class 1 and class 3 pixel values; (d) Results of segmentation of class 1 and class 2 pixel values; (e) Results of class 2 and class 3 pixel value segmentation; (f) The result of XOR of figure (d) and figure (e); (g) Results of figure (c), figure (d), figure (e) or operation; (h) The target obtained by removing the pseudo-connected components according to the area relationship of the connected components after inverting figure (g)
    Comparison of various segmentation methods. The picture corresponds to circle 3 in the first picture in the first row in Fig. 4. (a) Otsu; (b) Global histogram threshold using Otsu's method; (c) Multilevel image thresholds using Otsu’s method; (d) Iterative threshold segmentation method; (e) DRLSE algorithm[18]; (f) Proposed method
    Fig. 5. Comparison of various segmentation methods. The picture corresponds to circle 3 in the first picture in the first row in Fig. 4. (a) Otsu; (b) Global histogram threshold using Otsu's method; (c) Multilevel image thresholds using Otsu’s method; (d) Iterative threshold segmentation method; (e) DRLSE algorithm[18]; (f) Proposed method
    Sub-pixel edge detection ideal step model. (a) Original edge image; (b) Rotated edge image
    Fig. 6. Sub-pixel edge detection ideal step model. (a) Original edge image; (b) Rotated edge image
    Edge detail. (a) A corner of the checkerboard calibration board; (b) Zernike sub-pixel edges and thresholded edges after classification
    Fig. 7. Edge detail. (a) A corner of the checkerboard calibration board; (b) Zernike sub-pixel edges and thresholded edges after classification
    Diagram of pose solution
    Fig. 8. Diagram of pose solution
    Measurement error data of different methods under different exposures. (a) The standard deviation of measurement error of different methods; (b) Maximum deviation of measurement error of different methods
    Fig. 9. Measurement error data of different methods under different exposures. (a) The standard deviation of measurement error of different methods; (b) Maximum deviation of measurement error of different methods
    Zernike method attitude data at different positions of the same exposure. (a) Standard deviation of attitude; (b) Maximum deviation of attitude; (c) Mean attitude
    Fig. 10. Zernike method attitude data at different positions of the same exposure. (a) Standard deviation of attitude; (b) Maximum deviation of attitude; (c) Mean attitude
    The measurement error data of different size masks under different exposures. (a) The standard deviation of the measurement error of different size masks; (b) The maximum deviation of the measurement error of different size masks
    Fig. 11. The measurement error data of different size masks under different exposures. (a) The standard deviation of the measurement error of different size masks; (b) The maximum deviation of the measurement error of different size masks
    Guangbao Tian, Jian Wang, Bowen Wang. Monocular camera non-cooperative target extraction and pose detection[J]. Infrared and Laser Engineering, 2021, 50(12): 20210166
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