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- W2897665770 abstract "Aiming at1 the problem that the image segmentation effect of canola flower is not ideal under natural light conditions. In this paper, the color similarity judgment model is established based on RGB color space. After several experiments, the appropriate color similarity judgment threshold is determined to classify the canola flower image according to color, in order to complete the segmentation of canola flower. In order to verify the validity of the algorithm, 40 canola images at different flowering stages were taken for comparison experiments. The threshold segmentation based on EXG over-green index and the K-means clustering algorithm in Lab color space were used. Compared with the algorithm of this paper, the mean and variance of the segmentation error and classification error of this algorithm are only 5.65%, 0.65% and 0.82%, 0.2%, which are lower than the indexes of the other two algorithms. The experimental results show that the algorithm can effectively segment the canola flower target and adapt to the change of light intensity to ensure the integrity of the color, texture and shape of the canola flower. The findings for future research canola flower on automatic identification to provide reliable data." @default.
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- W2897665770 date "2018-10-22" @default.
- W2897665770 modified "2023-09-27" @default.
- W2897665770 title "Image Segmentation of Canola Based on Color Similarity in Color Space" @default.
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- W2897665770 doi "https://doi.org/10.1145/3207677.3278078" @default.
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