Features construction for starfruit quality inspection

TL;DRAbstract

Up to present, the starfruit quality inspection process is performed manually. Manual inspection will cause inconsistency in quality due to human subjective nature, slow processing and labor intensive. Hence, this thesis presents automation process development for the starfruit quality inspection in terms of techniques and algorithms design based on image processing. Basically, there are three main processes of the starfruit quality inspection discussed in this thesis, which are the maturity index classification, skin defect estimation and shape defect estimation. Throughout these processes, new features constructed based on colors and shape are proposed. In maturity index classification, a two-color feature, M, is proposed to differentiate six maturity indices of the starfruit. With the two-color feature, one third of computational data is reduced compared to the typical 3-color features. For skin defect estimation process, a new gray level co-occurrence matrix (GLCM) statistical feat

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