Araştırma Makalesi
Comparison of the visual texture calculation methods by image analysis, applied to mirror and scaled carp skin
Öz
Regions of interest (ROI) representative of the visual texture of images of mirror carp Cyprinus carpio carpio and scaled carp Cyprinus carpio were taken. Red, green, blue and grayscale (R, G, B, GS) histograms of these ROI were calculated. The following methods of visual texture calculations were performed on the ROIs: 1) image energy based on histograms, 2) image entropy based on histograms, 3) image energy based on co-occurrence matrices, 4) image entropy based on co-occurrence matrices, 5) texture based on fractal dimensions, 6) texture based on texture primitives method. Calculations were performed for color and grayscale images. The identification of the smoothest and roughest ROIs depended on the method used. The largest range between the minimum and maximum values was found in the co-occurrence matrix-based entropy calculation. A close second was the texture change index (TCI) method.
Anahtar Kelimeler
Cyprinus carpio visual texture histograms co-occurrence matrices fractals
Destekleyen Kurum
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Proje Numarası
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Teşekkür
The author would like to thank Prof. Dr. Murat O. Balaban for his invaluable technical support and Mr. Serkan ERKAN, Director of Republic of Turkey Ministry of Agriculture and Forestry, Mediterranean Fisheries Research Production and Training Institute, Antalya, Turkey.
Kaynakça
- Alçiçek, Z. & Balaban, M.O. (2012). Development and application of “The Two Image” method for accurate object recognition and color analysis. Journal of Food Engineering, 111(1), 46-51. DOI:10.1016/j.jfoodeng.2012.01.031
- Balaban, M. O. (2008). Quantifying non-homogeneous colors in agricultural materials. Part I: Method development. Journal of Food Science, 73(9), 431-437. DOI: 10.1111/j.1750-3841.2008.00807.x
- Balaban, M. O., Stewart, K., Fletcher, G. C. & Alçiçek, Z. (2014). Color change of the snapper (Pagrus auratus) and gurnard (Chelidonichthys kumu) skin and eyes during storage: effect of light polarization and contact with ice. Journal of Food Sciences, 79(12), E2456-2479. DOI: 10.1111/1750-3841.12693
- Basset, O., Buquet, B., Abuelkaram, S., Delachartre, P. & Culioli, J. (2000). Application of texture image analysis for the classification of bovine meat. Food Chemistry, 69, 437-445.
- Bharati, M.H., Liu, J.J. & MacGregor, J.F. (2004). Image texture analysis: methods and comparisons. Chemometrics and Intelligent Laboratory Systems, 72 (1), 57-71. DOI: 10.1016/j.chemolab.2004.02.005
- Gümüş, E., Yılayaz, A., Kanyılmaz, M., Gümüş, B. & Balaban., M. (2021). Evaluation of body weight and color of cultured European catfish (Silurus glanis) and African catfish (Clarias gariepinus) using image analysis. Aquacultural Engineering, 93, 102147. DOI:10.1016/j.aquaeng.2021.102147
- Hendrawan, Y., Fauzi, M.R., Khoirunnisa, N. S., Andreane, M., Hartianti, P. O., Halim, T. D. & Umam, C. (2019). Development of colour co-occurrence matrix (CCM) texture analysis for biosensing, IOP Conference Series: Earth and Environmental Science, 230, 012022. DOI:10.1088/1755-1315/230/1/012022
- Larkin, K.G. (2016). Reflections on Shannon information: In search of a natural information-entropy for images. [Online]. Available: https://arxiv.org/abs/1609.01117 (01.05.2021).
- Luzuriaga, D.A., Balaban, M.O. & Yeralan, S. (1997). Analysis of visual quality attributes of white shrimp by machine vision. Journal of Food Sciences, 62(1), 113-119. DOI: 10.1111/j.1365-2621.1997.tb04379.x
- Oliveira, A. C. M., Crapo, C. & Balaban, M. O. (2006). Grading of pink salmon skin watermarking using a machine vision system. Second Joint Transatlantic Fisheries Technology Conference. October 29–November 1, 2006, Quebec City, Quebec, Canada. P-46, p. 138.
- Padmavathi, K. & Thangadurai, K. (2016). Implementation of RGB and grayscale images in plant leaves disease detection- comparative study. Indian Journal of Science and Technology. 9(6). DOI: 10.17485/ijst/2016/v9i6/77739, February 2016
- Partio, M., Cramariuc, B., Gabbouj, M. & Visa, A. (2002). Rock Texture Retrieval Using Gray Level Co-Occurrence Matrix. 5th Nordic Signal Processing Symposium, Oct. 2002.
- Pathak, B. & Barooah, D. (2013). Texture analysis based on the gray level co-occurrence matrix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy, 2(9), 4206-4212.
- Perez-Nieto, A., Chanona-Perez, J. J., Farrera-Rebollo, R. R., Gutierrez-Lopez, G. F., Alamilla-Beltran, L. & Calderon-Dominguez, G. (2010). Image analysis of structural changes in dough during baking. LWT- Food Science and Technology, 43, 535-543.
- Sharma, M., Markou, M. & Singh, S. (2001). Evaluation of texture methods for image analysis. in Intelligent Information Systems Conference, The Seventh Australian and New Zealand IEEE, 117–121.
- Tamura, H., Mori, S. & Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics, SMC-8 (6), 460-473.
- Tuceryan, M. (1998). Texture analysis. In” The handbook of Pattern Recognition and Computer Vision, 2nd edition”, Chen C H, Pau L F, Wang P S P, editors. Chapter 2.1 p: 207-248. World Scientific Publishing Co.
- Varma, M. & Garg, R. 2007. Locally invariant fractal features for statistical texture classification. IEEE 11th International Conference on Computer Vision. 1-8. IEEEXPLORE.IEEE.ORG.
- Zheng, C., Sun, D.W. & Zheng L. (2006). Recent developments and applications of image features for food quality evaluation and inspection – a review. Trends in Food Science Technology, 17, 642-655. DOI: 10.1016/j.tifs.2006.06.005
- Zheng, C., Sun, D.W. & Zheng, L. (2007). A new region-primitive method for classification of colour meat image texture based on size, orientation, and contrast. Meat Science, 76, 620-627. DOI: 10.1016/j.meatsci.2007.02.003
- Zucker, S.W. & Terzopoulos, D. (1980). Finding structure in co-occurrence matrices for texture analysis. Computer Graphics and Image Processing, 12, 286-308. DOI: 10.1016/0146-664X(80)90016-7
Yıl 2021,
Cilt: 38 Sayı: 3, 383 - 391, 15.09.2021
Öz
Proje Numarası
-
Kaynakça
- Alçiçek, Z. & Balaban, M.O. (2012). Development and application of “The Two Image” method for accurate object recognition and color analysis. Journal of Food Engineering, 111(1), 46-51. DOI:10.1016/j.jfoodeng.2012.01.031
- Balaban, M. O. (2008). Quantifying non-homogeneous colors in agricultural materials. Part I: Method development. Journal of Food Science, 73(9), 431-437. DOI: 10.1111/j.1750-3841.2008.00807.x
- Balaban, M. O., Stewart, K., Fletcher, G. C. & Alçiçek, Z. (2014). Color change of the snapper (Pagrus auratus) and gurnard (Chelidonichthys kumu) skin and eyes during storage: effect of light polarization and contact with ice. Journal of Food Sciences, 79(12), E2456-2479. DOI: 10.1111/1750-3841.12693
- Basset, O., Buquet, B., Abuelkaram, S., Delachartre, P. & Culioli, J. (2000). Application of texture image analysis for the classification of bovine meat. Food Chemistry, 69, 437-445.
- Bharati, M.H., Liu, J.J. & MacGregor, J.F. (2004). Image texture analysis: methods and comparisons. Chemometrics and Intelligent Laboratory Systems, 72 (1), 57-71. DOI: 10.1016/j.chemolab.2004.02.005
- Gümüş, E., Yılayaz, A., Kanyılmaz, M., Gümüş, B. & Balaban., M. (2021). Evaluation of body weight and color of cultured European catfish (Silurus glanis) and African catfish (Clarias gariepinus) using image analysis. Aquacultural Engineering, 93, 102147. DOI:10.1016/j.aquaeng.2021.102147
- Hendrawan, Y., Fauzi, M.R., Khoirunnisa, N. S., Andreane, M., Hartianti, P. O., Halim, T. D. & Umam, C. (2019). Development of colour co-occurrence matrix (CCM) texture analysis for biosensing, IOP Conference Series: Earth and Environmental Science, 230, 012022. DOI:10.1088/1755-1315/230/1/012022
- Larkin, K.G. (2016). Reflections on Shannon information: In search of a natural information-entropy for images. [Online]. Available: https://arxiv.org/abs/1609.01117 (01.05.2021).
- Luzuriaga, D.A., Balaban, M.O. & Yeralan, S. (1997). Analysis of visual quality attributes of white shrimp by machine vision. Journal of Food Sciences, 62(1), 113-119. DOI: 10.1111/j.1365-2621.1997.tb04379.x
- Oliveira, A. C. M., Crapo, C. & Balaban, M. O. (2006). Grading of pink salmon skin watermarking using a machine vision system. Second Joint Transatlantic Fisheries Technology Conference. October 29–November 1, 2006, Quebec City, Quebec, Canada. P-46, p. 138.
- Padmavathi, K. & Thangadurai, K. (2016). Implementation of RGB and grayscale images in plant leaves disease detection- comparative study. Indian Journal of Science and Technology. 9(6). DOI: 10.17485/ijst/2016/v9i6/77739, February 2016
- Partio, M., Cramariuc, B., Gabbouj, M. & Visa, A. (2002). Rock Texture Retrieval Using Gray Level Co-Occurrence Matrix. 5th Nordic Signal Processing Symposium, Oct. 2002.
- Pathak, B. & Barooah, D. (2013). Texture analysis based on the gray level co-occurrence matrix considering possible orientations. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy, 2(9), 4206-4212.
- Perez-Nieto, A., Chanona-Perez, J. J., Farrera-Rebollo, R. R., Gutierrez-Lopez, G. F., Alamilla-Beltran, L. & Calderon-Dominguez, G. (2010). Image analysis of structural changes in dough during baking. LWT- Food Science and Technology, 43, 535-543.
- Sharma, M., Markou, M. & Singh, S. (2001). Evaluation of texture methods for image analysis. in Intelligent Information Systems Conference, The Seventh Australian and New Zealand IEEE, 117–121.
- Tamura, H., Mori, S. & Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics, SMC-8 (6), 460-473.
- Tuceryan, M. (1998). Texture analysis. In” The handbook of Pattern Recognition and Computer Vision, 2nd edition”, Chen C H, Pau L F, Wang P S P, editors. Chapter 2.1 p: 207-248. World Scientific Publishing Co.
- Varma, M. & Garg, R. 2007. Locally invariant fractal features for statistical texture classification. IEEE 11th International Conference on Computer Vision. 1-8. IEEEXPLORE.IEEE.ORG.
- Zheng, C., Sun, D.W. & Zheng L. (2006). Recent developments and applications of image features for food quality evaluation and inspection – a review. Trends in Food Science Technology, 17, 642-655. DOI: 10.1016/j.tifs.2006.06.005
- Zheng, C., Sun, D.W. & Zheng, L. (2007). A new region-primitive method for classification of colour meat image texture based on size, orientation, and contrast. Meat Science, 76, 620-627. DOI: 10.1016/j.meatsci.2007.02.003
- Zucker, S.W. & Terzopoulos, D. (1980). Finding structure in co-occurrence matrices for texture analysis. Computer Graphics and Image Processing, 12, 286-308. DOI: 10.1016/0146-664X(80)90016-7
Toplam 21 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Gıda Mühendisliği |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | - |
| Yayımlanma Tarihi | 15 Eylül 2021 |
| Gönderilme Tarihi | 11 Mayıs 2021 |
| Yayımlandığı Sayı | Yıl 2021Cilt: 38 Sayı: 3 |