EKSTRAKSI CIRI TEKSTUR CITRA KULIT SAPI BERBASIS CO-OCCURRENCE MATRIX
Abstract
dilakukan dalam pengolahan citra karena dari hasil
ekstraksi ciri bisa diperoleh informasi penting mengenai
karakteristik citra tersebut. Salah satu ciri yang bisa
dianalisis adalah ciri tekstur.
Pada ekstraksi ciri tekstur kulit sapi tersamak (leather),
digunakan metode statistikal berbasis Gray Level Cooccurrence
Matrix (GLCM). Dari sampel kulit yang
ada, diambil data berupa citra berwarna berukuran
256x256 piksel. Selanjutnya citra tersebut diubah
menjadi citra abu-abu. Pada proses ekstraksi ciri,
terhadap citra abu-abu tersebut dilakukan sejumlah
eksperimen dengan mengubah-ubah level keabuan dan
sudut dalam membentuk GLCM untuk mendapatkan
nilai yang paling tepat.
Terdapat lima ciri yang digunakan yaitu contrast,
correlation, energy, entropy, dan homogeneity. Untuk
mendapatkan ekstraksi ciri yang tepat, dari hasil
percobaan didapatkan nilai gray level adalah 64 dan
sudut adalah 0.
Full Text:
PDFReferences
Anil K. Jain, Fundamentals of digital image processing.Prentice-Hall, Inc., 1989
B. Wang, H. Wang, and H. Qi, “Wood recognition based ongrey-level co-occurrence matrix,” in 2010 InternationalConference on Computer Application and System Modeling(ICCASM), 2010, vol. 1, pp. V1–269–V1–272
K.-S. Loke and M. Cheong, “Efficient textile recognition viadecomposition of co-occurrence matrices,” in 2009 IEEEInternational Conference on Signal and Image ProcessingApplications (ICSIPA), 2009, pp. 257–261
R. A. Pramunendar, C. Supriyanto, D. H. Novianto, I. N.Yuwono, G. F. Shidik, and P. N. Andono, “A classificationmethod of coconut wood quality based on Gray Level Cooccurrencematrices,” in 2013 IEEE International Conferenceon Robotics, Biomimetics, and Intelligent ComputationalSystems (ROBIONETICS), 2013, pp. 254–257
L. Nanni, S. Brahnam, S. Ghidoni, E. Menegatti, and T.Barrier, “A comparison of methods for extracting informationfrom the co-occurrence matrix for subcellular classification,”Expert Syst. Appl., vol. 40, no. 18, pp. 7457–7467, Dec. 2013
L. Fu and B. Zhang, “A Co-occurrence Matrix algorithm usedfor medical image,” in 2011 International Conference onComputer Science and Network Technology (ICCSNT), 2011,vol. 2, pp. 1318–1321
M. H. Bharati, J. J. Liu, and J. F. MacGregor, “Image textureanalysis: methods and comparisons,” Chemom. Intell. Lab.Syst., vol. 72, no. 1, pp. 57–71, Jun. 2004
RC Gonzalez and RE Woods, Digital Image Processing. 1993
R. M. Haralick, K. Shanmugam, and I. Dinstein, “TexturalFeatures for Image Classification,” IEEE Trans. Syst. ManCybern., vol. SMC-3, no. 6, pp. 610–621, Nov. 1973
J. Zou and C.-C. Liu, “Texture Classification by Matching CooccurrenceMatrices on Statistical Manifolds,” in 2010 IEEE10th International Conference on Computer and InformationTechnology (CIT), 2010, pp. 1–7
Ramesh Jain, Rangachar Kasturi, and Brian G. Schunck,Machine Vision. McGraw-Hill, Inc., 1995
Q. Wang, H. Liu, J. Liu, and T. Wu, “A new method for leathertexture image classification,” in , Proceedings of the IEEEInternational Symposium on Industrial Electronics, 1992,1992, pp. 304–307 vol.1
F. P. Lovergine, A. Branca, G. Attolico, and A. Distante,“Leather inspection by oriented texture analysis with amorphological approach,” in , International Conference onImage Processing, 1997. Proceedings, 1997, vol. 2, pp. 669–671 vol.2.
W. P. Amorim, H. Pistori, M. C. Pereira, and M. A. C. Jacinto,“Attributes Reduction Applied to Leather DefectsClassification,” in 2010 23rd SIBGRAPI Conference onGraphics, Patterns and Images (SIBGRAPI), 2010, pp. 353–359.
R. M. Haralick, “Statistical and structural approaches totexture,” Proc. IEEE, vol. 67, no. 5, pp. 786–804, May 1979
J.-H. Liu, C.-Y. Wang, Q. Gao, Y. Liu, and Z.-K. Chen,“Research and implementation for texture of handback skinquantitative analysis based on co-occurrence matrix,” inInternational Conference on Industrial Mechatronics andAutomation, 2009. ICIMA 2009, 2009, pp. 158–161.
Refbacks
- There are currently no refbacks.