Image Matching Method Based on Multi-scale Corner Detection

Gao, Jing; Cai, Xingfu

doi:10.1109/cis.2017.00035

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…The conversion between the coordinate points of the two images can be realized by using similarity transformation matrix. For infrared cameras and visible light cameras with parallel optical axes, the registration error is mainly caused by the object distance [11] , as shown in equation ( 2):…”

Section: 2methods Of Image Registrationmentioning

confidence: 99%

A composite image recognition based approach for spontaneous combustion point detection and positioning in coal piles

Zhou

et al. 2022

J. Phys.: Conf. Ser.

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The phenomenon of spontaneous combustion of coal piles often occurs in closed coal yards of power plants, automatic fire-fighting equipments are generally used to detect and locate the spontaneous combustion point. However, their performance (false alarm rate) are largely limited due to the complicated environment of the coal yard. To deal with this problem, this paper proposes a composite image recognition based method for detecting and positioning the spontaneous combustion point (SCP) in coal yard. Yolov5s is trained to efficiently detect SCPs and infrared information is fused to distinguish the realiability of SCP detection result via composite image registration method. In addition, an angle compensation algorithm is proposed to adjust the spray angle of the equipment for positioning the cooling area. The experimental results show that after integrating the above related technologies, the false alarm rate of the spontaneous combustion point in the scene is greatly reduced, and the work efficiency is effectively improved.

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Section: 2methods Of Image Registrationmentioning

confidence: 99%

A composite image recognition based approach for spontaneous combustion point detection and positioning in coal piles

Zhou

et al. 2022

J. Phys.: Conf. Ser.

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“…Angular point is an important local characteristic of image. These pixels contain rich two-dimensional structure information which gray-scale transformation value high enough in all directions [19,20]. Harris corner detection algorithm is more effective in feature extraction and it only used the first-order difference of gray and filtering.…”

Section: Harris Corner Detection Principlementioning

confidence: 99%

Target Tracking Algorithm Using Angular Point Matching Combined with Compressive Tracking

Luo

Dong

et al. 2015

Advances in OptoElectronics

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To solve the problems of tracking errors such as target missing that emerged in compressive tracking (CT) algorithm due to factors such as pose variation, illumination change, and occlusion, a novel tracking algorithm combined angular point matching with compressive tracking (APMCCT) was proposed. A sparse measurement matrix was adopted to extract the Haar-like features. The offset of the predicted target position was integrated into the angular point matching, and the new target position was calculated. Furthermore, the updating mechanism of the template was optimized. Experiments on different video sequences have shown that the proposed APMCCT performs better than CT algorithm in terms of accuracy and robustness and adaptability to pose variation, illumination change, and occlusion.

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“…Scale invariant feature theory is introduced into the Harris feature detection algorithm in literature [5] and [6], to realize the scale invariant of Harris feature corner detection, but this algorithm can not provide a large number of stable feature points. Through the…”

Section: Introductionmentioning

confidence: 99%

Harris Scale Invariant Corner Detection Algorithm Based on the Significant Region

Wu¹,

Xu²,

Li³

et al. 2016

IJSIP

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Image Matching Method Based on Multi-scale Corner Detection

Cited by 8 publications

References 3 publications

A composite image recognition based approach for spontaneous combustion point detection and positioning in coal piles

A composite image recognition based approach for spontaneous combustion point detection and positioning in coal piles

Target Tracking Algorithm Using Angular Point Matching Combined with Compressive Tracking

Harris Scale Invariant Corner Detection Algorithm Based on the Significant Region

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