A New Automatic Seamless Image Stitching Algorithm Based on the Gray Value of Edges

Cao, Jian; Xu, Lingfeng; Guo, Shan; Ding, De Xiang

doi:10.4028/www.scientific.net/amm.496-500.2241

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…The image scale space extreme point detection method can extract the structure surface's natural texture features. Image scale-invariant feature transform algorithm (SIFT) [11,12] can extract natural texture's extreme points in the scale space of the image through the method of image's gray difference, called feature points. The obtained feature points have scale, position, and orientation invariant features.…”

Section: Methods Of Feature Point Trackingmentioning

confidence: 99%

Computer-Vision-Based Structure Shape Monitoring of Bridges Using Natural Texture Feature Tracking

Zhu

Chu

Duan

2023

Lecture Notes in Civil Engineering

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Structural health monitoring is carried out for a limited number of measuring points of the bridge. The root of the problem of bridge damage identification is that the mechanical equation inversion result is not unique due to the incomplete measured data. The full-field description ability of digital images to the structural shapes can effectively alleviate the problem of damage identification caused by incomplete measured data. This project aims to research the full-field shape monitoring method of the bridge. Firstly, the mathematical representation of the corresponding image points on the bridge surface is formed by using the image feature extraction method. Analyze the feature points position change mathematical model before and after deformation, and propose the structural full-field displacement vector calculation theory. Verify the full-field displacement calculation theory by a test beam. The results show that the maximum absolute error of the vector length is 0.24 mm and the relative error is less than 5%. The research realizes the structural full-field displacement monitoring under the natural texture condition for the first time. The results can promote the application and development of digital image processing technology in the field of structural health monitoring, improve the level of bridge safety evaluation, and realize the automation, intelligence and visualization of structural deformation monitoring.

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Section: Methods Of Feature Point Trackingmentioning

confidence: 99%

Computer-Vision-Based Structure Shape Monitoring of Bridges Using Natural Texture Feature Tracking

Zhu

Chu

Duan

2023

Lecture Notes in Civil Engineering

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Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification

et al. 2023

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Currently, measurement points in bridge structural health monitoring are limited. Consequently, structural damage identification is challenging due to sparse monitoring data. Hence, a structural full-field displacement monitoring and damage identification method under natural texture conditions is proposed in this work. Firstly, the feature points of a structure were extracted via image scale-invariant feature transform. Then, the mathematical model was analyzed respecting the relative position change of the feature points before and after deformation, and a calculation theory was proposed for the structure’s full-field displacement vector (FFDV). Next, a test beam was constructed to obtain the FFDV calculation results for the beam under different damage conditions. Validation results showed that the maximum length error of the FFDV was 0.48 mm, while the maximum angle error was 0.82°. The FFDV monitoring results for the test beam showed that the rotation angle of the displacement vector at the damage location presented abnormal characteristics. Additionally, a damage identification index was proposed for the rotation-angle change rate. Based on the validation test, the index was proven to be sensitive to the damage location. Finally, a structural damage identification program was proposed based on the FFDV monitoring results. The obtained results will help to expand structural health monitoring data and fundamentally solve damage identification issues arising from sparse monitoring data. This study is the first to implement structural full-field displacement monitoring under natural texture conditions. The proposed method exhibits outstanding economic benefits, efficiency, and visualization advantages compared with the conventional single-point monitoring method.

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A New Automatic Seamless Image Stitching Algorithm Based on the Gray Value of Edges

Cited by 2 publications

References 5 publications

Computer-Vision-Based Structure Shape Monitoring of Bridges Using Natural Texture Feature Tracking

Computer-Vision-Based Structure Shape Monitoring of Bridges Using Natural Texture Feature Tracking

Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification

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