| Titre : |
Region wise surface level defect detection and ranking of crust leather images based on image processing techniques |
| Type de document : |
texte imprimé |
| Auteurs : |
S. Nithiyanantha Vasagam, Auteur ; M. Sornam, Auteur |
| Année de publication : |
2023 |
| Article en page(s) : |
p. 282-292 |
| Note générale : |
Bibliogr. |
| Langues : |
Américain (ame) |
| Catégories : |
Analyse d'image L'analyse d'image est la reconnaissance des éléments contenus dans l'image. Il ne faut pas confondre analyse (décomposition en éléments) et traitement (action sur les composantes) de l'image.
Croûte (cuir)On entend par "cuir en croûte" des cuirs ayant subi les opérations jusqu'au tannage, à l'exclusion de toute opération de corroyage ou de finissage, mais qui, par opposition aux wet-blue ont été séchés.
Cuirs et peaux -- Défauts
Détection de défauts (Ingénierie)
Surfaces -- défauts
|
| Index. décimale : |
675 Technologie du cuir et de la fourrure |
| Résumé : |
Sorting and aligning of crust leather for grading on position wise defect distribution is one of the methods adopted in the tanning industry. This method is generally carried out manually by a veteran on official sampling position and their input is critical because it is directly linked to sales of the crust leather. The opinion of the experts is believed to be stable and consumes a good amount of time too. Hence, in the current research a robust defect detection method and ranking of crust leather images based on image processing techniques is proposed to give a stable solution in a short span of time. A custom-made dataset of crust leather images consisting of 5640 images were used in this study. The pixel intensity has been extracted on demarcated position of various regions including neck, belly left, belly right, center and butt instead of official sampling position through horizontal and vertical mapping of coordinates with a new method Grading Score on Image Position wise (GSIP) on the actual images. The image processing techniques using Canny Edge Detection and filters such as Laplacian, Median, Prewitt, Roberts, Sobel and Scharr were implemented to get the pixel intensity grouped and classified based on parameters within acceptable range using a Naïve Bayes Classifier. The classifier confirms that the accuracy of Set I - Actual Images and Set II - Defects with implementation of canny edge detection over other image processing techniques at 99.50%. Therefore, the current research confirms that the proposed GSIP method would give an additional tool to inspectors while ranking the crust leather based on region wise surface level defect detection of crust leather images based on image processing techniques. |
| Note de contenu : |
- Dataset of crust leather images
- Regions wise horizontal and vertical mapping
- Feature extraction
- Generation of ranking matrix
- Classification with naive bayes
- Table 1 : Summary of Authors, Techniques and Area
- Table 1 : Sample 6×6 Matrix representation of pixel points where i is row, j is column and n represent the size = 6
- Table 2 : Representation of three region mapping of pixel point as Row Size (RS) = n/3
- Table 3 : Cutting area and corresponding Grading
- Table 4 : Intermittent Leather grading based on area of usefulness based on demerit count
- Table 5 : Ranking based on Pixel intensity value associated with the co-ordinates of horizontal (Row i) and vertical (Column j)
- Table 6 : Coordinates for an image of 250 × 250
- Table 7 : Demarcated position of various regions within the hide for surface defect detection
- Table 8 : Ranking of Dataset I and II
- Table 9 : Naïve Bayes Classifier Comparison of various image files |
| DOI : |
https://doi.org/10.34314/jalca.v118i7.7856 |
| En ligne : |
https://drive.google.com/file/d/1KlGnwGbGA8ZA6a6wO5y74ab-LV-tcsz4/view?usp=drive [...] |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=39662 |
in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA) > Vol. CXVIII, N° 7 (07/2023) . - p. 282-292
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