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A Fast and Robust Approach for Touching Grains Segmentation

2018; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-93000-8_54

1611-3349

Peterson Adriano Belan, Robson Aparecido Gomes de Macedo, Marihá M. A. Pereira, Wonder Alexandre Luz Alves, Sidnei Alves de Araújo,

Imbalanced Data Classification Techniques

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).