Image Analysis-Based Estimation of Metallic and Pearl Add-Ons Concentrations in Automotive Paints

• Autorzy: Ślot K. , Strzecha K. , Nowak G.
• Języki publikacji: EN

Abstrakty

EN

The paper reports results of preliminary research on automotive paint dopant concentration assessment based on microscopic image segmentation. The considered task is illconditioned due to the richness and diversity in contents of images to be analyzed. The proposed procedure involves two main phases: image segmentation, where focal-plane paint addons are extracted from the background, and object analysis and classification. The results of experimental verification of the proposed method on a set of eighteen paint pigmented images (black and yellow) show that the estimation can be done with approximately 5% accuracy for paints doped with only single addon type. For add-on mixtures, the results were strongly dependent on pigment color and mutual add-on proportions.

Słowa kluczowe

EN

microscopic image segmentation; object analysis

• Wydawca: Instytut Telekomunikacji i Informatyki Uniwersytetu Technologiczno-Przyrodniczego w Bydgoszczy
• Czasopismo: Image Processing & Communications
• Rocznik: 2014
• Tom: Vol. 19, no. 2-3
• Strony: 119--126
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Ślot K.

• Institute of Applied Computer Science, Lodz University of Technology

autor

Strzecha K.

• Institute of Applied Computer Science, Lodz University of Technology

autor

Nowak G.

• Institute of Applied Computer Science, Lodz University of Technology

Bibliografia

• [1] Dougherty, E.R., Astola J. (1993). Mathematical Non-Linear Image Processing, Kluwer Academic Publishers, London
• [2] Haindl, M., Filip, J. (2013). Visual Texture. Accurate Material Appearance Measurement, Representation and Modeling, Springer
• [3] Julesz, B. (1981). Textons, the elements of texture perception and their interaction, Nature 290, 91-97
• [4] Manjunath, B.S., Ma, W.Y. (1996). Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 18(8), 837-42
• [5] Petrou, M., Sevilla, P.G. (2006). Image Processing: Dealing With Texture, Wiley
• [6] Ro, Y.M., Kim, M., Kang, H.K., Manjunath, B.S., Kim, I. (2001). MPEG-7 Homogeneous Texture Descriptor, ETRl Journal, 23(2)
• [7] Sun, J. (2006). Edge Detection, Image Segmentation and Their Applications in Microarray Image Analysis, Proquest/UMI
• [8] Suri, J.S., Wilson, D., Laxminaryan, S. (2005). Handbook of Biomedical Image Analysis. Volume 1: Segmentation Models, Springer
• [9] Tamura, H., Mori, S. Yamawaki, T. (1978). Textural Features Corresponding to Visual Perception, IEEE Trans. On Systems, Man, and Cybernetics, 8(6), 460-473
• [10] Woods, J.W. (2006). Multidimensional Signal, Image, and Video Processing and Coding, Academic Press Inc. Orlando
• [11] Yoo, T.S., (2004). Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis, AK Peters
• [12] Zhang, Y.J., (2006). Advances in Image and Video Segmentation, IRM Press