Fast Corner Detection Based on Multi-Directional Structure Tensor

Ning Li; Junfeng Jing; Weichuan Zhang; Mengmeng Bai; Jiurui Sun

doi:10.3788/LOP202158.2015005

Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 20 >Page 2015005 > Article

Laser & Optoelectronics Progress
Vol. 58, Issue 20, 2015005 (2021)

Fast Corner Detection Based on Multi-Directional Structure Tensor

Ning Li, Junfeng Jing^*, Weichuan Zhang, Mengmeng Bai, and Jiurui Sun

Author Affiliations

School of Electronic Information, Xi’an Polytechnic University, Xi’an, Shaanxi 710048, China

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DOI: 10.3788/LOP202158.2015005 Cite this Article Set citation alerts

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 Copy Citation Text

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Fig. 1. Integral image schematic diagram

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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

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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

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Fig. 4. Construction process of integral image

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Fig. 5. Proposed corner detection algorithm

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Fig. 6. Test image set for the evaluation about repeatability under affine transformation

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Fig. 7. Average repeatability of the proposed method under different parameters. (a) Rotation; (b) shear transform

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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

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Fig. 9. Test images and their ground truth corner positions. (a) Laboratory; (b) geometric image; (c) block image

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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-P_n)+(48+6)×P_n	(7×7+5)×P_n	P_n

Table 1. Influence of the coarse selection mechanism on the construction of corner measurement map

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 2. Average results for detectors under repeatability under affine transformation

Test image	Method	False corner number	Missed corner number	Localization error	Detected number
Laboratory	Zhang and Sun	105	68	1.957	286
Laboratory	Proposed	70	90	1.885	229
Geometric image	Zhang and Sun	1	27	0.889	58
Geometric image	Proposed	6	25	1.255	65
Block image	Zhang and Sun	4	18	1.906	45
Block image	Proposed	0	16	2.040	43

Table 3. Detection performance of different algorithms

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

Table 4. Running time of different algorithms unit: s

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

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