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- W2896081884 abstract "The main objective of this work is to propose a method, for the identification of crack in melon, using the colour images, and classify into cracked and non-cracked ones, and giving the dominant colours of the surface. The melon is segmented, and its cracked part is identified using k-means clustering based segmentation algorithm. Colour images of melons, with differently shaped cracks, are taken as input dataset for employing the proposed algorithm. Colour components in the image of melon are used for segmentation. k-means clustering based approach is an iterative process that is used to cluster images of melon into k clusters. Pixels are clustered after transformation into L*a*b* colour space taking as an input a* and b* channels, and images are produced to identify the cracked part. A cluster containing the cracked part was found by the value of the centroid. Additionally, the melon's surface dominant colours are obtained for the main peel colour representation. The experimental study shows the effectiveness of proposed method on the applied dataset of melon's images provided by Vilmorin & Company." @default.
- W2896081884 created "2018-10-26" @default.
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- W2896081884 date "2018-09-01" @default.
- W2896081884 modified "2023-10-13" @default.
- W2896081884 title "Melon Crack Identification and Classification Using k-Means Clustering for Quality Inspection" @default.
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- W2896081884 doi "https://doi.org/10.1109/cvcs.2018.8496659" @default.
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