Acta Optica Sinica, Volume. 41, Issue 12, 1215001(2021)
Particle Streak Velocimetry Method Based on Binocular Vision and Multiple Exposure
Fig. 2. Comparison of trajectory endpoints between different recognition methods. (a) Image of a trajectory; (b) result of image binarization and skeletonizing (threshold for 0.1); (c) result of gray-level fitting
Fig. 4. Schematic diagrams of binocular matching of multi-exposure PSV images. (a) Trajectories from the left camera; (b) trajectories from the left camera; (c) drawing of partial enlargement
Fig. 6. Image processing process. (a) Image after distortion correction; (b) recognition of short exposure points; (c) recognition of long trajectories; (d) extracted original image of every sub-trajectory; (e) image of obtaining endpoints from gray-level fitting; (f) image of multiple trajectories matching
Fig. 8. Trajectory images for the condition of 0° and 60 mm/s. (a) Image from the left camera; (b) image from the right camera
Fig. 9. Measurement results and standard deviations under different setting velocities. (a) Rail is parallel to the imaging plane of the left camera; (b) rail has an angle of 30° with the imaging plane of the left camera
Fig. 11. Experimental measurement results. (a) Image from the left camera; (b) image from the right camera; (c) identified trajectories on the left image; (d) identified trajectories on the right image; (e) trajectories of particles in three-dimensional coordinate system; (f) disparity map
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Wu Zhou, Fangting Wang, Xiaoxiao Wang, Xinran Tang, Xiaoshu Cai. Particle Streak Velocimetry Method Based on Binocular Vision and Multiple Exposure[J]. Acta Optica Sinica, 2021, 41(12): 1215001
Category: Machine Vision
Received: Nov. 10, 2020
Accepted: Jan. 18, 2021
Published Online: Jun. 2, 2021
The Author Email: Wu Zhou (zhouwu@usst.edu.cn)