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- W4200352757 abstract "Segmentation is a necessary process related to the implementation of computer vision. In the agriculture field, this process is required, such as for automatic harvesting system and classification of maturity fruit. This process becomes more complex and challenging when implemented on the natural background, where the object is mingled with the others. This study aims to develop a tomato segmentation method on the natural background. The method performed three main processes, including localization of tomato, pre-processing, and segmentation. Canny edge detection and reconstruction were applied in the process of localization and segmentation. The evaluation was carried out against the segmentation method using 100 tomato images with three maturity levels: raw, half-ripe, and ripe. The method's performance was measured based on parameters of segmentation accuracy, the error rate of false positive and false negative, which achieved the value of 89.74%, 0.78%, and 9.48%, respectively." @default.
- W4200352757 created "2021-12-31" @default.
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- W4200352757 date "2021-11-24" @default.
- W4200352757 modified "2023-10-18" @default.
- W4200352757 title "Tomato Segmentation on Natural Background Using Multi Operation of Edge Detection And Reconstruction" @default.
- W4200352757 doi "https://doi.org/10.1109/urucon53396.2021.9647316" @default.
- W4200352757 hasPublicationYear "2021" @default.
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