Improved GrabCut-based method for coffee leaf segmentation

Miaomiao Yang; Jingcong Li

doi:10.1117/12.3034130

13 June 2024 Improved GrabCut-based method for coffee leaf segmentation

Miaomiao Yang, Jingcong Li

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318026 (2024) https://doi.org/10.1117/12.3034130
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

This study proposes a GrabCut algorithm to address the segmentation challenges in coffee leaf images. The algorithm integrates the Contrast Limited Adaptive Histogram Equalization algorithm and the Gamma Correction technique. The leaf images undergo preprocessing using Contrast Limited Adaptive Histogram Equalization and Gamma Correction before being segmented with the GrabCut algorithm. Experimental evaluations are conducted using coffee leaf images, comparing results in terms of intuitive segmentation visualization and quantized indices such as PSNR, segmentation accuracy, false segmentation rate, and algorithm runtime. The findings indicate that the proposed algorithm exhibits greater robustness against variations in light conditions, resulting in higher segmentation accuracy. It outperforms classical three-class benchmark algorithms and effectively segments leaf images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Miaomiao Yang and Jingcong Li "Improved GrabCut-based method for coffee leaf segmentation", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318026 (13 June 2024); https://doi.org/10.1117/12.3034130

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