Infrared Sea-Sky Line Detection Utilizing Self-Adaptive Laplacian of Gaussian Filter and Visual-Saliency-Based Probabilistic Hough Transform

Fu, Jian; Zhao, Jianhui; Li, Fan

doi:10.1109/lgrs.2021.3111099

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…To bring out the performance of the network, the input image is subjected to Gaussian blurring to reduce the impact of noise [10] . Fig.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Wei,

Liu,

Wan

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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The new incidence rate of rectal cancer accounts for 12.2% of all cancers, which seriously affects the health of the people. In addition, the early clinical diagnosis of T staging of rectal cancer will strongly support the formulation of effective treatment plans. However, it is difficult for clinicians to accurately determine the T stage of rectal cancer before operation because the site of rectal tumor invasion is not obvious. Therefore, this paper first proposes a bilinear feature fusion mechanism, which effectively avoids the problem of information loss in the process of convolution neural network training of rectal cancer MRI images; Secondly, the new weighted loss function designed can solve the problem of multi-example imbalance; Finally, the experiment proves that the constructed intelligent prediction strategy for Tstage of rectal cancer has a good accuracy, which provides a good auxiliary result for the clinical treatment of rectal cancer.

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“…To bring out the performance of the network, the input image is subjected to Gaussian blurring to reduce the impact of noise [10] . Fig.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Wei,

Liu,

Wan

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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“…The Recursive filter can estimate the power spectral density of the signal and noise according to the correlation between the input signal and the output signal, and then use these estimates to calculate the parameters of the filter. Finally, merge the two images to obtain an image that can both eliminate noise spots and preserve prominent cracks on the solar panel [16], [17], [18]. The construction of commonly used biological vision models is relatively complex.…”

Section: A Model For Identifying Surface Defects Of Solar Panels a Pe...mentioning

confidence: 99%

Surface Defect Recognition of Solar Panel Based on Percolation-Based Image Processing and Serre Standard Model

Xia

et al. 2023

IEEE Access

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During the production process of solar panels, it is inevitable to have some defects, such as cracks on the surface of solar panels due to extrusion or damage due to quality issues. This article improves the Serre standard model, which can simulate the ventral visual pathway with object recognition ability, based on the latest research progress and results of simulating biological visual mechanism models in computer vision, to improve the recognition effect of surface defects on solar panels. At the same time, a pre-processing scheme combining Gaussian Laplace operator operator and adaptive Wiener filter to remove noise spots is studied, and the local Gabor Binary Pattern Histogram Sequence (LGBPHS) features are obtained through pre-processing. The Percolation-Based image processing method for detecting obvious cracks was used to determine the location of the algorithm and the calculation results based on the improved standard model method. It mainly refers to the MAX value output by the C2 layer and the classification and identification results of whether there are cracks, and the crack location function is completed. The experimental results show that the proposed method has an accuracy rate of 98.86% in training and 98.64% in testing, and both the false detection rate and the missed detection rate do not exceed 1%. Therefore, the method proposed in the study has a high accuracy and can effectively identify the surface defects of solar panels.INDEX TERMS Image processing, solar panels, defect recognition, local binary mode.

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“…They use it to detect lines from infrastructure landmarks in real-time. Fu et al [24] propose a visual-saliency-based probabilistic Hough transform (VSBPHT) to detect sea-sky lines. They use PHT to extract the candidate line segments and refine the detection results by visual saliency features.…”

Section: Introductionmentioning

confidence: 99%

A new hough transform operated in a bounded cartesian coordinate parameter space

Yang

Hou

et al. 2022

IET Image Processing

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In this paper, a new Hough transform is proposed to detect lines with geometric meanings. In the standard Hough transform, lines are parameterized by the length and orientation of the normal vector from the origin to the line. The parameter space of the standard Hough transform is presented in a polar coordinate system. Geometric measurements such as distance and angle are not suitable in polar coordinate space due to the inconsistency of the axes. To deal with this problem, a Hough transform which is carried out in a bounded Cartesian coordinate parameter space is developed. The lines are represented by their perpendicular feet with respect to a fixed point. Since the parameter space is in Cartesian coordinate space, it is intuitive to the researchers. When detecting the peaks in the parameter space, distance and angle constraints can be applied. The effectiveness of the proposed method has been illustrated by two applications, that is, lane line detection and convex polygon extraction. The results show that the proposed Cartesian coordinate Hough transform is suitable for detecting meaningful lines.

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Infrared Sea-Sky Line Detection Utilizing Self-Adaptive Laplacian of Gaussian Filter and Visual-Saliency-Based Probabilistic Hough Transform

Cited by 11 publications

References 17 publications

Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

Surface Defect Recognition of Solar Panel Based on Percolation-Based Image Processing and Serre Standard Model

A new hough transform operated in a bounded cartesian coordinate parameter space

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