Markerless Image Alignment Method for Pressure-Sensitive Paint Image

Suzuki, K.; Inoue, Tomoki; Nagata, Takayuki; Kasai, Morio; Nonomura, Taku; Matsuda, Yu

doi:10.3390/s22020453

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…As preprocessing, flood filling ( Figure 18 c) and image rotation ( Figure 18 d) were applied [ 6 ], after iterative binarization ( Figure 18 b) of the grayscale image ( Figure 18 a). For an objective performance analysis, the performance of the proposed method was compared with those of the line-based [ 7 , 14 ] and corner-based reference point extraction methods [ 8 , 9 , 14 ], among the existing reference point search algorithms [ 8 , 9 , 14 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts

Moon¹,

Kim

2022

Sensors

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For products such as smartphones, the technology gap between companies is gradually narrowing with the advancements in technology. Therefore, product design can be a visible strategy for differentiation. However, it is difficult to apply automated production and defect detection processes to the various designs that are being developed. This study proposes a high-speed circular measurement method for correcting the alignment of round parts, which is difficult in an automated process. For analyzing the performance of the proposed method, its processing speed and accuracy are compared with those of the existing methods. The results of the analysis indicate that the overall performance of the proposed method is better than those of the existing methods.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts

Moon¹,

Kim

2022

Sensors

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show abstract

“…Thereby, the correspondence between the key points in the two images is established. Suzuki et al [10] proposed a markerless registration method using the Moore-Neighbor tracking algorithm to detect the boundary of the PSP and using boundary points as feature points. Experiments showed that this method has a lower computational cost than the difference-of-Gaussian detector [7] while maintaining good registration accuracy.…”

Section: Introductionmentioning

confidence: 99%

A deep-learning image registration method for pressure-sensitive paint measurements

Di,

Gu,

Liu

et al. 2024

Meas. Sci. Technol.

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Image registration is an essential step in the data processing of pressure-sensitive paint (PSP) measurements. As PSP technology is gradually expanded to increasingly harsh environments, it faces challenges such as severe image misalignment due to model deformations/motions, which pose difficulties to traditional featurebased registration algorithms. To improve registration accuracy and efficiency, we propose an end-to-end image registration method based on deep learning. Initially, a PSP dataset based on PSP images is constructed through data augmentation. Three types of residual network and three training strategies are then adopted to prepare the deep-learning model for automatic image registration. The optimal combination of the residual network and training strategy is selected for validation using fabricated PSP images and outperforms two traditional algorithms (i.e., the Sift and Watershed methods). Finally, the performance of the deep-learning method is compared with that of traditional algorithms adopting a new metric of the overlapping rate for assessing the registration accuracy on experimental PSP images. The results show that the deep learning method outperforms the traditional algorithms in terms of registration accuracy and robustness.

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All-day Image Alignment for PTZ Surveillance Based on Correlated Siamese Neural Network

Hu,

Zheng,

Wang

et al. 2023

SIViP

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Markerless Image Alignment Method for Pressure-Sensitive Paint Image

Cited by 4 publications

References 44 publications

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts

A deep-learning image registration method for pressure-sensitive paint measurements

All-day Image Alignment for PTZ Surveillance Based on Correlated Siamese Neural Network

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