Nonlint material identification using computer vision and pattern recognition

摘要

Abstract: per discusses methods used to evaluate a feature space for identification of non-lint material (trash) in cotton samples. A main criterion for accepting any feature in the identification task was invariance under translation, rotation, and, in most cases, scale. In subsequent processing, most features were normalized. Classical grouping was performed in an n-dimensional feature space using divisive hierarchical clustering based on the Euclidian distance metric. The best results for identifying bark, stick, and leaf/pepper trash in the sample data set was 92%. By category, bark was identified correctly 88%, stick 84%, and leaf/pepper 94% of the time. Identification between leaf and pepper could be handled by defining an area cutoff in the pepper- leaf continuum. !13