Annals of Computer Science and Information Systems, Volume 15

DOI: http://dx.doi.org/10.15439/2018F286

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 101–105 (2018)

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Abstract. Varietal homogeneity is an important factor in quality of malting barley, but its inspection is difficult. Biochemical methods are expensive and inefficient, while machine vision suffers due to high variability of the grains' features. In our previous work, we have shown a convolutional neural network for simultaneous feature extraction and classification of image data basing on multiple views. It was suggested that for machine vision inspection, the observed side of a grain should be taken into account -- dorsal and ventral sides of each kernel exhibit different features. In this study we present a viewpoint-aware convolutional neural network, which learns to extract specialized features from images of dorsal and ventral sides of barley grains. We show that it increases the average classification accuracy by 0.6\% and sensitivity by 2.3\% with respect to the viewpoint-ignorant architecture on our dataset.

References

1. P. Zapotoczny, M. Zielinska, and Z. Nita, “Application of image analysis for the varietal classification of barley:,” Journal of Cereal Science, vol. 48, no. 1, pp. 104–110, Jul. 2008. http://dx.doi.org/10.1016/j.jcs.2007.08.006
2. K. Nowakowski, P. Boniecki, R. J. Tomczak, S. Kujawa, and B. Raba, “Identification of malting barley varieties using computer image analysis and artificial neural networks,” presented at the Fourth International Conference on Digital Image Processing (ICDIP 2012), 2012, vol. 8334, p. 833425. http://dx.doi.org/10.1117/12.954155
3. B. Hailu and M. Meshesha, “Applying Image Processing for Malt-barley Seed Identification,” presented at the Conference: Ethiopian the 9th ICT Annual Conference 2016 (EICTAC 2016), Addis Ababa, 2016.
4. P. M. Szczypiński, A. Klepaczko, and P. Zapotoczny, “Identifying barley varieties by computer vision,” Computers and Electronics in Agriculture, vol. 110, pp. 1–8, Jan. 2015. http://dx.doi.org/10.1016/j.compag.2014.09.016
5. S. H. Lee, C. S. Chan, P. Wilkin, and P. Remagnino, “Deep-plant: Plant identification with convolutional neural networks,” in 2015 IEEE International Conference on Image Processing (ICIP), pp. 452–456. http://dx.doi.org/10.1109/ICIP.2015.7350839
6. P. Dolata, M. Mrzygłód, and J. Reiner, “Double-stream Convolutional Neural Networks for Machine Vision Inspection of Natural Products,” Applied Artificial Intelligence, vol. 31, no. 7–8, pp. 643–659, Sep. 2017. http://dx.doi.org/10.1080/08839514.2018.1428491
7. P. Lampa, M. Mrzygłód, and J. Reiner, “Methods of manipulation and image acquisition of natural products on the example of cereal grains.,” Control & Cybernetics, vol. 45, no. 3, 2016.
8. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.
9. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research, vol. 15, pp. 1929–1958, 2014.
10. Y. Jia et al., “Caffe: Convolutional Architecture for Fast Feature Embedding,” 2014, pp. 675–678. http://dx.doi.org/10.1145/2647868.2654889
11. I. Sutskever, J. Martens, G. Dahl, and G. Hinton, “On the Importance of Initialization and Momentum in Deep Learning,” in Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, Atlanta, GA, USA, 2013, pp. III–1139–III–1147.