Scale factor in digital cameras

Badali, Anthony P.; Zhang, Yahui; Carr, Peter; Thomas, Paul J.; Hornsey, Richard

doi:10.1117/12.632215

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After selecting ROIs, feature points (P n ) within the ROIs are extracted by either the Shi-Tomasi corner detector [36] or the KAZE feature detector [37] and are registered for identifying the initial position of the subject in pixel coordinates as denoted P n p x, 1 , p y,1 . The multiple key points P n p x, i , p y,i detected in the ith frame are matched with the registered features and tracked by implementing the KLT tracker algorithm [38][39][40]. The Maximum Likelihood Estimation Sample Consensus (MLESAC) method [41] is used to define dominant geometric transformation and to eliminate outliers amongst the matched features for consistent displacement vectors between the reference and ith images.…”

Section: Feature Point-based Measurement Systemmentioning

confidence: 99%

See 1 more Smart Citation

A Feasibility Study on Extension of Measurement Distance in Vision Sensor Using Super-Resolution for Dynamic Response Measurement

Cho,

Gong

2023

Sensors

View full text Add to dashboard Cite

The current civil infrastructure conditions can be assessed through the measurement of displacement using conventional contact-type sensors. To address the disadvantages of traditional sensors, vision-based sensor measurement systems have been derived in numerous studies and proven as an alternative to traditional sensors. Despite the benefits of the vision sensor, it is well known that the accuracy of the vision-based displacement measurement is largely dependent on the camera extrinsic or intrinsic parameters. In this study, the feasibility study of a deep learning-based single image super-resolution (SISR) technique in a vision-based sensor system is conducted to alleviate the low spatial resolution of image frames at long measurement distance ranges. Additionally, its robustness is evaluated using shaking table tests. As a result, it is confirmed that the SISR can reconstruct definite images of natural targets resulting in an extension of the measurement distance range. Additionally, it is determined that the SISR mitigates displacement measurement error in the vision sensor-based measurement system. Based on this fundamental study of SISR in the feature point-based measurement system, further analysis such as modal analysis, damage detection, and so forth should be continued in order to explore the functionality of SR images by applying low-resolution displacement measurement footage.

show abstract

Section: Feature Point-based Measurement Systemmentioning

confidence: 99%

“…the KLT tracker algorithm [38][39][40]. The Maximum Likelihood Estimation Sample Consensus (MLESAC) method [41] is used to define dominant geometric transformation and to eliminate outliers amongst the matched features for consistent displacement vectors between the reference and 𝑖th images.…”

Section: Feature Point-based Measurement Systemmentioning

confidence: 99%