Improvement of noise filtering and image registration methods for the Pressure Sensitive Paint experiments

Fujimatsu, Nobuyoshi; Tamura, Yoshiaki; Fujii, Kozo

doi:10.1007/bf03181500

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The alignment of the reference and the run images is an important procedure in the post-processing of PSP data, because the misalignment of the images leads to large measurement error. In conventional techniques, black markers placed on a model surface are used to image alignment [32,33]. Sant et al [32] proposed an automatic method that detects black markers and links those of the reference and the run images.…”

Section: Introductionmentioning

confidence: 99%

“…The linking method is based on the distance between the markers, and the method requires an iterative calculation. Since the positions of the markers in both reference and run images are detected and linked manually in the method proposed by Fujimatsu et al [33], the method is time consuming for practical applications. The advantage of the method proposed by Fujimatsu et al also uses corners of a model for the image alignment to improve the alignment accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Markerless Image Alignment Method for Pressure-Sensitive Paint Image

Suzuki

Inoue

Nagata

et al. 2022

Sensors

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We propose a markerless image alignment method for pressure-sensitive paint measurement data replacing the time-consuming conventional alignment method in which the black markers are placed on the model and are detected manually. In the proposed method, feature points are detected by a boundary detection method, in which the PSP boundary is detected using the Moore-Neighbor tracing algorithm. The performance of the proposed method is compared with the conventional method based on black markers, the difference of Gaussian (DoG) detector, and the Hessian corner detector. The results by the proposed method and the DoG detector are equivalent to each other. On the other hand, the performances of the image alignment using the black marker and the Hessian corner detector are slightly worse compared with the DoG and the proposed method. The computational cost of the proposed method is half of that of the DoG method. The proposed method is a promising for the image alignment in the PSP application in the viewpoint of the alignment precision and computational cost.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Markerless Image Alignment Method for Pressure-Sensitive Paint Image

Suzuki

Inoue

Nagata

et al. 2022

Sensors

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“…These algorithms work well on datasets with good contrast between different regions, but they encounter difficulties with noisy images. To overcome this drawback, different procedures for noise reduction have been proposed (e.g., Fujimatsu et al, 2005). Region-based methods (Adams and Bischof, 1994) are based on the determination of homogeneous areas inside the image, by merging those pixels that satisfy certain connectivity and similarity criteria.…”

Section: Introductionmentioning

confidence: 99%

Application of color image segmentation to estrusc detection

Fresno

Macchi

Marti

et al. 2006

J Vis

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Automatic segmentation and classification of color images is a problem of great practical interest in different areas. This paper presents an algorithm for this purpose which is divided in three steps. Firstly, the regions of interest are isolated from the rest of the image based on threshold functions defined in the YUV and YIQ color spaces, producing a set of connected components. Then, a set of features is computed to enable a quantitative evaluation of the segmented objects. Finally, the image is classified by means of a decision rule based on the analysis of the differences between the computed measures and a set of ideally segmented images, according to experts' assessment. The algorithm was applied to a decision support tool for estrus detection in cattle. This approach constitutes a valuable alternative to improve this process, as it may replace the visual observation by the automatic analysis of pictures taken to cows in controlled environments. Experimental results show that the segmentations obtained with this method are highly satisfactory and they allow a precise classification of the images with low computational complexity.

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