Laser & Optoelectronics Progress, Volume. 57, Issue 1, 011204(2020)
Checkerboard Corner Detection Based on Corner Gray Distribution Feature
Fig. 2. Distributions of pixels on circle in each region. (a) Sum of pixels in any two adjacent regions of circle is half of sum of pixels on circle; (b) pixels at boundary on concentric circle correspond to the same θ
Fig. 3. Distributions of candidate corners before and after iterative refinement. (a) Distribution of candidate corners before iterative refinement; (b) locally enlarged drawing of
Fig. 6. Contrast experiments of corner detection. (a) Original images; (b) results of corner detection by Matlab Toolbox[15]; (c) results of corner detection by algorithm in Ref. [11]; (d) results of corner detection by proposed algorithm
Fig. 7. Checkerboard with large distortion. (a) Original images; (b) result of corner detection by Matlab Toolbox[15]; (c) result of corner detection by algorithm in Ref. [11]; (d) result of corner detection by proposed algorithm
Fig. 9. Re-projection error. (a) Matlab Toolbox[15]; (b) algorithm in Ref. [11]; (c) proposed algorithm (without refinement); (d) proposed algorithm (with refinement)
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Ming Wu, Junlong Wu, Shuai Ma, Kangjian Yang, Ping Yang. Checkerboard Corner Detection Based on Corner Gray Distribution Feature[J]. Laser & Optoelectronics Progress, 2020, 57(1): 011204
Category: Instrumentation, Measurement and Metrology
Received: Jul. 3, 2019
Accepted: Jul. 15, 2019
Published Online: Jan. 3, 2020
The Author Email: Yang Ping (pingyang2516@163.com)