Abstract
The visual properties of agricultural grains are important factors for determining their market prices and assisting their choices by consumers. Despite the importance of visual inspection processes for agricultural grains quality, such tasks are usually handled manually and therefore subject to many failures. Thus, a computer vision approach that is able to segment correctly the grains contained in an image for further classification and detection of defects consists of an important practical application, which can be employed by visual quality inspection systems. In this work we propose an approach based on mathematical morphology and correlation-based granulometry techniques, guided by a set of heuristics, for grains segmentation. Experimental results showed that the proposed approach is able to segment the grains contained in an image, with high accuracy and very low computational time, even in cases where there are many grains glued together (touching grains).
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Acknowledgements
The authors would like to thank UNINOVE, CNPq–Brazilian National Research Council for the research scholarship granted to S. A. Araújo (Proc. 311971/2015-6) and FAPESP–São Paulo Research Foundation (Proc. 2017/05188-9).
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Belan, P.A., de Macedo, R.A.G., Pereira, M.M.A., Alves, W.A.L., de Araújo, S.A. (2018). A Fast and Robust Approach for Touching Grains Segmentation. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_54
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