Fig 8 - uploaded by Chiraz Ajmi
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Detected lines and defect's line on canny image, (a) Applied to Fig (3.a); (b) Applied to Fig (3.b).
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Context 1
... note that those lines correspond to the two lines with min and the max ofρ. So we follow those steps: -We have eliminated the lines corresponding to the borders of the image; -We consider that the line with a minimum value of is the first external line detected in the same as shown in Fig.8; -We consider the first line with ρ superior to the minimum and inferior to the maximum is our defect. ...
Context 2
... is a structure array whose length equals the number of merged line segments found After that, we applied the procedure described in the section (C.2) to extract the line defect from the other detected lines. Results of segmentation are shown respectively in Fig.7 (a), (b), (c), and of location of linear defect by Hough Transform presented in Fig. 8. It can easily observe that applying the above segmentation steps our images are well segmented. Thus, we noted two classes of pixels in the image with the accentuation of defect's region, defect's zone is pronounced, and contours are outlined. PSNR value is calculated after process of segmentation and preprocessing image. We find that ...
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... All the processed images are in greyscale photos, where the size of each image is 320 × 120 pixels, 72 dpi, and 8-bit depth. Defects that are present in the high intensity area (or the region of interest) are low in intensity and thus are usually excluded from the region of interest (ROI) [30,31]. In this study, the proposed method is known as 'hybrid' because it combines the local and the global details information [23]. ...
Luminosity and contrast variation problems are among the most challenging tasks in the image processing field, significantly improving image quality. Enhancement is implemented by adjusting the dark or bright intensity to improve the quality of the images and increase the segmentation performance. Recently, numerous methods had been proposed to normalise the luminosity and contrast variation. A new approach based on a direct technique using statistical data known as Hybrid Statistical Enhancement (HSE) is presented in this study. The HSE method uses the mean and standard deviation of a local and global neighbourhood and classified the pixel into three groups; the foreground, border, and problematic region (contrast & luminosity). The datasets, namely weld defect images, were utilised to demonstrate the effectiveness of the HSE method. The results from the visual and objective aspects showed that the HSE method could normalise the lumi-nosity and enhance the contrast variation problem effectively. The proposed method was compared to the two (2) populor enhancement methods which is Homomorphic Filter (HF) and Difference of Gaussian (DoG). To prove the HSE effectiveness, a few image quality assessments were presented, and the results were discussed. The HSE method achieved a better result compared to the other methods, which are Signal Noise Ratio (8.920), Standard Deviation (18.588) and Absolute Mean Brightness Error (9.356). In conclusion, implementing the HSE method has produced an effective and efficient result for background correction and quality images improvement.
