The analysis of the quality of particulate materials is of great importance for a variety of research and industrial applications. Most image-based methods rely on the segmentation of the image to measure the particles and aggregate their characteristics. However, the segmentation of particulate materials can be severely affected when the setup is not controlled. For instance, when there are device errors, changes in the light conditions, or when the camera gets dirty because of the dust or a similar substance. All of these circumstances are common in industrial setups, like the one studied in this paper. This work presents a framework for quality estimation based on image processing algorithms that avoids segmentation. The considered application scenario is the online quality control of the production of Oriented Strand Boards (OSB), a type of wood panel frequently used in construction and manufacturing industries. The proposed method quantizes frequency domain into a histogram using a non-parametric method, which is later exploited using computational intelligence to classify the quality of superimposed wood particles deposed on a conveyor belt. The method has been tested using synthetic and real images with different noise conditions. The results illustrate the robustness of the approach and its capability to detect significant quality changes in the wood particles.

Analyzing images in frequency domain to estimate the quality of wood particles in OSB production / R. Donida Labati, A. Genovese, E. Munoz Ballester, V. Piuri, F. Scotti, G. Sforza - In: Computational Intelligence and Virtual Environments for Measurement Systems and ApplicationsPiscataway : Institute of Electrical and Electronics Engineers (IEEE), 2016. - ISBN 9781467397599. - pp. 24-29 (( convegno CIVEMSA tenutosi a Budapest nel 2016 [10.1109/CIVEMSA.2016.7524251].

Analyzing images in frequency domain to estimate the quality of wood particles in OSB production

R. Donida Labati;A. Genovese;E. Munoz Ballester;V. Piuri;F. Scotti;G. Sforza
2016

Abstract

The analysis of the quality of particulate materials is of great importance for a variety of research and industrial applications. Most image-based methods rely on the segmentation of the image to measure the particles and aggregate their characteristics. However, the segmentation of particulate materials can be severely affected when the setup is not controlled. For instance, when there are device errors, changes in the light conditions, or when the camera gets dirty because of the dust or a similar substance. All of these circumstances are common in industrial setups, like the one studied in this paper. This work presents a framework for quality estimation based on image processing algorithms that avoids segmentation. The considered application scenario is the online quality control of the production of Oriented Strand Boards (OSB), a type of wood panel frequently used in construction and manufacturing industries. The proposed method quantizes frequency domain into a histogram using a non-parametric method, which is later exploited using computational intelligence to classify the quality of superimposed wood particles deposed on a conveyor belt. The method has been tested using synthetic and real images with different noise conditions. The results illustrate the robustness of the approach and its capability to detect significant quality changes in the wood particles.
microscopy
Settore INF/01 - Informatica
INNOVATIVE POPLAR LOW DENSITY STRUCTURAL PANEL
Institute of Electrical and Electronics Engineers (IEEE)
Book Part (author)
File in questo prodotto:
File Dimensione Formato  
civemsa16.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione 281.07 kB
Formato Adobe PDF
281.07 kB Adobe PDF Visualizza/Apri
Analyzing_images_in_frequency_domain_to_estimate_the_quality_of_wood_particles_in_OSB_production.pdf

non disponibili

Tipologia: Publisher's version/PDF
Dimensione 1.48 MB
Formato Adobe PDF
1.48 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/427839
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 0
social impact