A self-adaptive correction method for perspective distortions of image

Wu, Li-Chung; Shang, Qinghua; Sun, Yupeng; Xu, Bo

doi:10.1007/s11704-018-7269-8

Cited by 18 publications

(13 citation statements)

References 19 publications

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“…Due to the large size of the solar panels, the camera cannot completely obtain a solar panel image horizontally; therefore, the camera needs to be tilted at an angle to the solar panel. However, the image captured from this angle is distorted, so, in this paper, a perspective transformation is firstly utilized to correct the distorted image, and the corrected image is shown in Figure 8b [23,24]. The corrected image is composed of 6 × 10 solar cells arranged neatly and stitched together.…”

Section: Image Preprocessingmentioning

confidence: 99%

Crack Extraction for Polycrystalline Solar Panels

Xue

Song

et al. 2021

Energies

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Crack extraction of solar panels has become a research focus in recent years. The cracks are small and hidden. In addition, there are particles of irregular shape and size on the surface of the polycrystalline solar panel, whose reflection position and direction are random. Therefore, there is a complex and uneven texture background on the solar panel image, which makes the crack extraction more difficult. In this paper, a crack extraction method combining image texture and morphological features is proposed. Firstly, the background texture and multi-scale details are suppressed by the linear filter and the Laplace pyramid decomposition method. Secondly, the edge can be extracted based on the modulus maximum method of the wavelet transform. Finally, cracks were extracted by using the improved Fuzzy C-means (FCM) clustering combining the morphological and texture features of the cracks. To make the extraction results more accurate and reasonable, an improved region growth algorithm is proposed to optimize the extraction results. All of the above research is closely centered on the accuracy and stability requirements of the solar cell crack detection, which is also the key point of this paper. The experimental results show that various improved or innovative algorithms proposed in this paper can accurately extract the position of cracks and obtain better extraction results. The detection results have good stability and can be faithful to the actual situation, which will promote the application of solar cells in more fields.

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Section: Image Preprocessingmentioning

confidence: 99%

Crack Extraction for Polycrystalline Solar Panels

Xue

Song

et al. 2021

Energies

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“…We can locate the bobbin by detecting an ellipse or circle in the image. However, the current circle-detection algorithms, such as Hough-Circle Transform [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], have some false detection. As such, we use a new method to detect bobbins.…”

Section: Preparationsmentioning

confidence: 99%

“…However, the image captured by the camera is deformed, and the round bobbin will turn into an ellipse in the image because the camera plane is not parallel to the bobbin’s end face plane, as shown in Figure 4 a. In order to establish the conversion relationship, the image needs to be restored using perspective transformation [ 14 ]. The conversion matrix is shown in Equation (1), where

are the pixel coordinates in original image, and

are the corresponding pixel coordinates in restored image.…”

Section: Preparationsmentioning

confidence: 99%

“…Transformation steps are as follows (we suppose that the bobbin edge has been detected and know the ellipse equation): Calculate the vertex coordinates

of the ellipse in the raw image, which is shown in Figure 4 c; The end face of bobbin is a circle, and its diameter in the transformed image is the major axis of the ellipse. As such, we can calculate the corresponding coordinates

of four vertexes in theory, which is shown in Figure 4 c; Bring the above coordinates into Equation (1), producing Equation (2) [ 14 ], so we can obtain the conversion matrix and then produce a restored image, as shown in Figure 4 d; Establish the conversion relationship between pixel size and real size via the bobbin’s pixel diameter and real diameter.

…”

Section: Preparationsmentioning

confidence: 99%

“…Bring the above coordinates into Equation (1), producing Equation (2) [ 14 ], so we can obtain the conversion matrix and then produce a restored image, as shown in Figure 4 d;…”

Section: Preparationsmentioning

confidence: 99%

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The Detection of Thread Roll’s Margin Based on Computer Vision

Shi

Wang

2021

Sensors

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The automatic detection of the thread roll’s margin is one of the kernel problems in the textile field. As the traditional detection method based on the thread’s tension has the disadvantages of high cost and low reliability, this paper proposes a technology that installs a camera on a mobile robot and uses computer vision to detect the thread roll‘s margin. Before starting, we define a thread roll‘s margin as follows: The difference between the thread roll‘s radius and the bobbin’s radius. Firstly, we capture images of the thread roll‘s end surface. Secondly, we obtain the bobbin’s image coordinates by calculating the image’s convolutions with a Circle Gradient Operator. Thirdly, we fit the thread roll and bobbin’s contours into ellipses, and then delete false detections according to the bobbin’s image coordinates. Finally, we restore every sub-image of the thread roll by a perspective transformation method, and establish the conversion relationship between the actual size and pixel size. The difference value of the two concentric circles’ radii is the thread roll’s margin. However, there are false detections and these errors may be more than 19.4 mm when the margin is small. In order to improve the precision and delete false detections, we use deep learning to detect thread roll and bobbin’s radii and then can calculate the thread roll’s margin. After that, we fuse the two results. However, the deep learning method also has some false detections. As such, in order to eliminate the false detections completely, we estimate the thread roll‘s margin according to thread consumption speed. Lastly, we use a Kalman Filter to fuse the measured value and estimated value; the average error is less than 5.7 mm.

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Region of Interest Extraction Based on Convolution Neural Networks for Image Linear Distortion Correction

Zhao

Ren

2020

Lecture Notes in Electrical Engineering

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A self-adaptive correction method for perspective distortions of image

Cited by 18 publications

References 19 publications

Crack Extraction for Polycrystalline Solar Panels

Crack Extraction for Polycrystalline Solar Panels

The Detection of Thread Roll’s Margin Based on Computer Vision

Region of Interest Extraction Based on Convolution Neural Networks for Image Linear Distortion Correction

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