Texture classification by statistical learning from morphological image processing: application to metallic surfaces
Open accessAbstract
Summary: A classification method based on textural information for metallic surfaces displaying complex random patterns is proposed. Because these kinds of textures show fluctuations at a small scale and some uniformity at a larger scale, a probabilistic approach is followed, considering textural variations as realizations of random functions. Taking into account information of pixel neighbourhoods, the texture for each pixel is described at different scales. By means of statistical learning, the most relevant textural descriptors are selected for each application. The performance of this approach is established on a real data set of steel surfaces.
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In a large number of instances, it has been possible to localise RoIs with a high degree of accuracy.
“In a large number of instances [3,35,41,42,56,58,61], it has been possible to localise RoIs with a high degree of accuracy.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [42]
The most popular algorithms applied to flat steel surface defect classification are PCA and LPP.
“Of these methods, the most popular algorithms applied to flat steel surface defect classification are PCA and LPP [112, 113].”
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- Authors
- CORD, A., BACH, F., & JEULIN, D.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Centre de Morphologie Mathématique - CMM (FR)
- Corresponding author
- JEULIN, D.
- ORCID
- Venue
- Journal of Microscopy
- Publisher
- Wiley
- Published
- 2010-07-14
- Volume
- 239
- Issue
- 2
- Pages
- 159-166
- ISSN
- 0022-2720 (ISSN-L)
- 1365-2818
- OpenAlex
- W2169628755
- Cited by
- 45
- References
- 30
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 3 · 2020: 6 · 2019: 4 · 2018: 2 · 2017: 3 · 2016: 4 · 2015: 3 · 2014: 4 · 2013: 2 · 2012: 2
- FWCI
- 7.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Microscopy (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Engineering › Computational Mechanics › Surface Roughness and Optical Measurements
- Keywords
- texture classification, metal surfaces, steel surfaces, statistical learning, mathematical morphology, random functions
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