An Investigation of Colour Properties of Orosei Limestones (Italy) by Using Computer Program

Article Preview

Abstract:

In this study, a new computer program developed to determine the colour properties of natural stone products is introduced. The program can scan any digitalised picture of natural stone products and produce several statistical results such as mean, variance, minimum and maximum colour values, skewness and kurtosis values of the colour histogram, energy, contrast, entropy and homogeneity values of the data which may be used to classify the typology and/or quality of natural stone products. As a case study, Orosei limestones (Sardegna, Italy) were used. There are several typologies of Orosei limestones such as Nuvolato (Cloudy), Venato chiaro (Light veined), Venato medio (Medium veined), Venato scuro (Dark veined), Perlato chiaro (Light pearled), Perlato medio (Medium pearled) and Perlato scuro (Dark pearled). The remarkable results produced by the program are presented.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

173-182

Citation:

Online since:

April 2013

Export:

Price:

[1] J. Lamas , A.J. López , A. Ramil , B. Prieto, T. Rivas: Monitoring the laser cleaning process of ornamental granites by means of digital image analysis, in Lasers in the Conservation of Artworks VIII, edited by Austin Nevin, CRC Press (2010).

DOI: 10.1201/b10567-17

Google Scholar

[2] T. Rivas, J.M. Matías, J. Taboada, C. Ordóñez: Functional experiment design for the analysis of colour changes in granite using new L*a*b* functional colour coordinates, Journal on Computational and Applied Mathematics, 235 (16) (2011).

DOI: 10.1016/j.cam.2010.08.005

Google Scholar

[3] M. Araújo, J. Martínez, C. Ordóñez, J.A.V. Vilán: Identification of Granite Varieties from Colour Spectrum Data, Sensors, 10 (9) (2010) pp.8572-8584.

DOI: 10.3390/s100908572

Google Scholar

[4] B. Jaehne, in: Practical handbook on image processing for scientific applications, 2nd edition, CRC Press (1997).

Google Scholar

[5] O. Akkoyun: An evaluation of image processing methods applied to marble quality classification, 2nd International Conference on Computer and Technology Development (ICCTD 2010), Cairo (2010).

DOI: 10.1109/icctd.2010.5646128

Google Scholar

[6] L. Cutaia, P. Massacci, I. Roselli: Analysis of Landsat 5 TM Images for Monitoring the State of Restoration of Abandoned Quarries, International Journal of Surface Mining, Reclamation and Environment, 18 (2) (2004) pp.122-134.

DOI: 10.1080/13895260412331295385

Google Scholar

[7] D. Karakus: Goruntu Analiz Yontemleri ile Kayacların Yapısal Ozelliklerinin Tanımlanması, Doktora tezi, DEU Fen B. Ens., Izmir (2006) (in Turkish).

Google Scholar

[8] G.R. Lane, C. Martin, E. Pirard: Techniques and applications for predictive metallurgy and ore characterization using optical image analysis, Minerals Engineering, 21 (7) (2008) pp.568-577.

DOI: 10.1016/j.mineng.2007.11.009

Google Scholar

[9] N.A. Baykan, N. Yılmaz: Mineral identification using color spaces and artificial neural networks, Computers & Geosciences, 36 (1) (2010) pp.91-97.

DOI: 10.1016/j.cageo.2009.04.009

Google Scholar

[10] N.H. Maerz: Aggregate sizing and shape determination using digital image processing, Center for Aggregates Research (ICAR) Sixth Annual Symposium Proceedings, St. Louis, Missouri, April 19-20 (1998) pp.195-203.

Google Scholar

[11] E. Cabello, M.A. Sanchez, J. Delgado: A New Approach to Identify Big Rocks with Applications to the Mining Industry, Real-Time Imaging 8 (1) (2002) pp.1-9.

DOI: 10.1006/rtim.2000.0255

Google Scholar

[12] N.H. Maerz, T.C. Palangio: Online Fragmentation Analysis for Grinding and Crushing Control, Control 2000 Symposium, 2000 SME Annual Meeting, March 1, Salt Lake City, Utah (2000) pp.109-116.

Google Scholar

[13] J.P. Latham, J. Kemeny, N. Maerz, M. Noy, J. Schleifer, S. Tose: A Blind Comparison Between Results of Four Image Analysis Systems Using a Photo-Library of Piles of Sieved Fragments, Fragblast: Int. Journal for Blasting and Fragmentation, 7 (2) (2003).

DOI: 10.1076/frag.7.2.105.15899

Google Scholar

[14] J. Kemeny, J. Handy: Improving blast fragmentation prediction with new technologies for rock mass characterization, Proceedings of the 30th Annual Conference on Explosives and Blasting Technique, International Society of Explosive Engineers, New Orleans (2004).

Google Scholar

[15] J.A. Sanchidrian, P. Segarra, L.M. Lopez: A Practical Procedure for the Measurement of Fragmentation by Blasting by Image Analysis, Rock Mechanics and Rock Engineering, 39 (4) (2006) pp.359-382.

DOI: 10.1007/s00603-005-0073-4

Google Scholar

[16] M. Bailey, C.O. Gomez, J.A. Finch: Development and application of an image analysis method for wide bubble size distributions, Minerals Engineering, 18 (12) (2005) pp.1214-1221.

DOI: 10.1016/j.mineng.2005.07.019

Google Scholar

[17] J.J. Liu, J.F. MacGregor, C. Duchesne, G. Bartolacci: Flotation froth monitoring using multiresolutional multivariate image analysis, Minerals Engineering, 18 (1) (2004) pp.65-76.

DOI: 10.1016/j.mineng.2004.05.010

Google Scholar

[18] Z. Ekmekci, A.N. Sahin: Kopuk goruntusu ve flotasyon performansı arasındaki iliskinin goruntu analiz sistemi ile incelenmesi, Madencilik, Cilt 45, Sayı 2, Sayfa 27-38 (2006) (in Turkish).

Google Scholar

[19] M.K. Gokay, I.B. Gundogdu: Color identification of some Turkish marbles, Construction and Building Materials, 22 (7) (2008) pp.1342-1349.

DOI: 10.1016/j.conbuildmat.2007.04.016

Google Scholar

[20] M. Deviren, M.K. Balci, U.M. Leloglu, M. Severcan: A Feature Extraction Method for Marble Tile Classification, Proceedings of 3rd Int. Conference on Computer Vision, Pattern Recognition and Image Processing (CVPRIP), Feb. 27 - Mar. 3, Atlantic City (2000).

Google Scholar

[21] L. Carrino, W. Polini, S. Turchetta: An automatic visual system for marble tile classification", Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 216 (8) (2002) pp.1095-1108.

DOI: 10.1243/095440502760272377

Google Scholar

[22] J. Martinez-Alajarin, J. D. Luis-Delgado, L.M. Tomás-Balibrea: Automatic system for quality-based classification of marble textures, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 35 (4) (2005) pp.488-497.

DOI: 10.1109/tsmcc.2004.843236

Google Scholar

[23] I. Ar, Y.S. Akgul: A generic system for the classification of marble tiles using Gabor filters", Proceedings of 23rd of the International Symposium on Computer and Information Sciences (ISCIS, 08), Istanbul (2008) pp.1-6.

DOI: 10.1109/iscis.2008.4717915

Google Scholar

[24] R.J. Schalkoff, in: Digital image processing and computer vision, John Wiley and Sons, NewYork: (1989) p.489.

Google Scholar

[25] M. Lustrino, L. Melluso, V. Morra: The transition from alkaline to tholeiitic magmas: a case study from the Orosei–Dorgali Pliocene volcanic district (NE Sardinia, Italy), Lithos, 63 (1-2) (2002) pp.83-113.

DOI: 10.1016/s0024-4937(02)00113-5

Google Scholar

[26] A. Russo, G. Sirna: Nota preliminare sul Malm di Cala Gonone (Golfo di Orosei, Sardegna), Geologica Romana, 25 (1986) p.165–179 (in Italian).

Google Scholar

[27] G. Siotto: Piano di recupero generale dell'intero comparto produttivo del marmo di Orosei, con proposta di regolamentazione e sviluppo della Zona D- industriale (destinata a cave e stabilimenti per la lavorazione dei lapidei) - Studio effettuato per il Comune di Orosei (Polo estrattivo e Distretto Industriale), Giugno 2006 (in Italian).

DOI: 10.3280/su20011-130006

Google Scholar

[28] N. Careddu, G. Siotto: Promoting ecological sustainable planning for natural stone quarrying. The case of the Orosei Marble Producing Area in Eastern Sardinia, Resources Policy, 36 (4) (2011) p.304–314.

DOI: 10.1016/j.resourpol.2011.07.002

Google Scholar