Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2015005(2021)
Fast Corner Detection Based on Multi-Directional Structure Tensor
Figures & Tables(13)
Fig. 2. An example of box filter. (a) Anisotropic directional derivative filter (θ=π/2); (b) corresponding box filter template and its size is 9×9
Fig. 3. Templates of box filter with six filter orientations. (a) θ=0; (b) θ=π/6; (c) θ=π/3; (d) θ=π/2; (e) θ=2π/3; (f) θ=5π/6
Fig. 6. Test image set for the evaluation about repeatability under affine transformation
Fig. 7. Average repeatability of the proposed method under different parameters. (a) Rotation; (b) shear transform
Fig. 8. Average repeatability under six image transformations for ten detectors. (a) JPEG compression; (b) rotation; (c) additive white Gaussian noises; (d) non-uniform scaling; (e) shear transform; (f) uniform scaling
Fig. 9. Test images and their ground truth corner positions. (a) Laboratory; (b) geometric image; (c) block image
Table 1. Influence of the coarse selection mechanism on the construction of corner measurement map
View tableTable 1. Influence of the coarse selection mechanism on the construction of corner measurement map
Method Addition operation Multiplication operation Division operation Without rough screening 5×M×N+(48+6)×M×N (7×7+5)×M×N M×N Rough screening 5×(M×N-Pn)+(48+6)×Pn (7×7+5)×Pn Pn
Table 2. Average results for detectors under repeatability under affine transformation
View tableTable 2. Average results for detectors under repeatability under affine transformation
Method Rotation Non-uniform scaling Shear Uniform scaling JPEG compression Gaussian noise Average ANDD 0.702 0.631 0.647 0.649 0.785 0.763 0.696 CPDA 0.794 0.773 0.784 0.767 0.809 0.832 0.793 DOG 0.772 0.710 0.684 0.794 0.734 0.695 0.732 FAST 0.795 0.771 0.780 0.769 0.811 0.822 0.791 Method Rotation Non-uniform scaling Shear Uniform scaling JPEG compression Gaussian noise Average Harris 0.793 0.675 0.731 0.686 0.725 0.608 0.703 Harris-Laplace 0.776 0.708 0.639 0.778 0.748 0.739 0.731 KAZE 0.751 0.791 0.674 0.809 0.701 0.673 0.733 LF-Net 0.565 0.675 0.648 0.640 0.852 0.790 0.695 Zhang and Sun 0.865 0.785 0.790 0.819 0.886 0.872 0.836 Proposed 0.862 0.814 0.820 0.831 0.901 0.881 0.852
Table 3. Detection performance of different algorithms
View tableTable 3. Detection performance of different algorithms
Test image Method False corner number Missed corner number Localization error Detected number Laboratory Zhang and Sun 105 68 1.957 286 Proposed 70 90 1.885 229 Geometric image Zhang and Sun 1 27 0.889 58 Proposed 6 25 1.255 65 Block image Zhang and Sun 4 18 1.906 45 Proposed 0 16 2.040 43
Table 4. Running time of different algorithms unit: s
View tableTable 4. Running time of different algorithms unit: s
Method Block(256 pixel× 256 pixel) Laboratory(512 pixel× 512 pixel) Boat(850 pixel× 680 pixel) Bike(1000 pixel× 700 pixel) Tree(700 pixel× 1000 pixel) Harris 0.112 0.434 0.951 1.172 1.212 Harris-Laplace 0.124 0.451 1.614 1.874 1.945 ANDD 0.882 4.637 11.285 6.594 11.268 Zhang and Sun 2.184 8.362 23.735 35.469 36.289 Proposed 0.075 0.279 0.675 0.762 0.756
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Ning Li, Junfeng Jing, Weichuan Zhang, Mengmeng Bai, Jiurui Sun. Fast Corner Detection Based on Multi-Directional Structure Tensor[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015005
Paper Information
Category: Machine Vision
Received: Nov. 2, 2020
Accepted: Jan. 20, 2021
Published Online: Oct. 14, 2021
The Author Email: Junfeng Jing (jingjunfeng0718@sina.com)
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