This paper focuses on the use of imaged-based machine learning techniques for identifing grain. In particular we
compare several texture descriptors based on Local Binary Patterns(LBP),and we report new experiments using a set of
novel texture descriptors based on the combination of the Elongated Quinary Pattern (EQP), the Elongated Ternary
Pattern (ELTP) and the Elongated Binary Patterns(ELBP).These three variants of the standard LBP are obtained by
considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local
gray-scale difference. The resulting extracted features are then used for training a machine-learning classifier(support
vector machine). Our results show that a local approach based on the EQP feature extractor, which can express both local
and holistic features of the grain image, produces a reliable system for identifing grain.
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