Recognition and Classification of Similar Looking Food Grain Images using Artificial Neural Networks
|Published in:||Issue 2, (Vol. 6) / 2012|
|Abstract.||This paper presents an recognition and classification of similar looking food grain images using artificial neural networks. Schemes for visual classification usually proceed in two stages. First, features are extracted which represents the image and Second, a classifier is applied to the extracted features to reach a decision regarding the represented type of images. We have considered four pairs of eight different types of similar looking commonly available Indian food grain images namely Jira, Badesoup, Mongdaal Woduddal. Ragi, Mustard, Soya, and Alasandi. The algorithms are developed to extract 18 color and 27 texture features. A Back Propagation Neural Network (BPNN) is used to classify and recognize the Food grain image samples using three different types of feature sets, viz, color, texture, combination of both color and texture features. The study reveals that the combination of color and texture features are out performed the individual color and texture features in recognition and classification of different similar looking food grain images samples.|
|Keywords:||Similar Looking Food Grain Images, Feature Extraction, Artificial Neural Networks.|
1. B. S. Anami, Dayanand G. Savakar,(2009), Effect of Foreign Bodies on Recognition and Classification of Bulk Food Grains Image Samples, Journal of Applied Computer Science and Mathematics, Volume 3(6), Pages: 77- 83,
2. Anami, B.S., Burkpalli,V., Angadi, S.A. Patil, N.M. (2003), “Neural network approach for grain classification and gradation”, Proceedings of the second national conference on document analysis and recognition, held at Mandya , India, during 11-12 July, 2003., pp. 394-408.
3. Amy L. Tabb, Donald L. Peterson and Johnny Park (2006),” Segmentation of Apple Food grain from Video via Background Modeling “ Proceedings of American Society of Agricultural and Biological Engineers (ASABE) Annual International Meeting held at Oregon Convention Center Portland, Oregon during 9 - 12 July 2006.
4. B. S. Anami, D. G. Savakar, Aziz Makandar, and P. H. Unki (2008).” A neural network model for classification of bulk grain samples based on color and texture”. Proceedings of International Conference on Cognition and Recognition, held at mandya, India, on 22 & 23 December 2008 .pp. 389 – 368.
5. B. S. Anami, D. G. Savakar, P. H. Unki and S.S. Sheelawant (2006). “An Artificial Neural Network Model for Separation, Classification and Gradation of Bulk Grain samples”. IEEE First International Conference on Signal and Image Processing held at Hubli , India during 7-9 December 2006 .pp 811—820.
6. Domingo Mery and Franco Pedreschi (2008)” Segmentation of colour food images using a robust algorithm“ Journal of food engineering, vol 66,pp 383-360.
7. Kazuhiro Nakano(1997) “ Application of neural networks to the color grading of apples “ Computers and Electronics in Agriculture ,18 , pp 108-116.
8. Kivanç Kiliç, Ismail Hakki Boyac, Hamit Köksel and Ismail Küsmenoglu ( 2006) “A classification system for beans using computer vision system and artificial neural networks.”, Journal of Food Engineering, volume 78, Issue 3, pp 897-904.
9. Libin Zhang, Qinghua Yang, Yi Xun, Xiao Chen, Yongxin Ren, Ting Yuan, Yuzhi Tan and Wei Li (2007), “Recognition of greenhouse cucumber Food grain using computer vision” New Zealand Journal of Agricultural Research, vol. 80: pp 1293–1298
10. McCollum,Visen, N.S., Paliwal, J., Jayas, D.S., White, N.D.G. (2004). “Image analysis of bulk grain samples using neural networks”, Canadian Biosystems Engineering, vol. 46, pp. 7.11-7.18.
11. P. H Heinemann, R. Hughes, C . T. Morrow, H.J Sommer, III, R.B. Beelman and P. J Wuest(1994)” Grading of Mushrooms using a machine vision system “ , American Society of Agricultural Engineers , vol 37(8) ,pp 1671-1677.
12. Patrick M. Mehl, Yud-Ren Chen, Moon S . Kim and Diane E. Chan(2004),” Development of hyper spectral imaging technique for detection of apple surface defects and contaminations”, Journal of food engineering, vol 61,pp 67-81.
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