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19 July 2024 A multi-feature image fusion method for rice weed segmentation
Jing Wang, Tiantian Wang, Yijie Tang
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Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132130I (2024) https://doi.org/10.1117/12.3035182
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China
Abstract
Rice occupies a significant proportion of the national food security system, and weeds are an important factor affecting rice yield. In the natural environment, the collection of rice field images will be due to the weather, light intensity changes and other factors, resulting in the details of the image are not obvious enough, thus making the image segmentation accuracy is not high. This paper proposes an adaptive histogram equalization method for this problem, which automatically adjusts the parameters according to the luminance distribution of the local region to improve the contrast of the image and retain the image details. In order to improve the segmentation accuracy, this paper proposes Laplace pyramid image fusion, which fuses the original image with the improved image, and is able to naturally and smoothly fuse the two images while preserving the image details. In order to suppress noise and avoid single features being affected by noise, which leads to inaccurate segmentation results, this paper proposes to use the method of fusion of color features and morphological features to enhance the segmentation accuracy of rice and weeds. The experimental results show that the Accuracy of the image segmentation method proposed in this paper reaches 96.9%, Precision reaches 93.3%, Recall reaches 95.8% and MIoU reaches 86.5%.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jing Wang, Tiantian Wang, and Yijie Tang "A multi-feature image fusion method for rice weed segmentation", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132130I (19 July 2024); https://doi.org/10.1117/12.3035182
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KEYWORDS
Image fusion
Image segmentation
Image processing
Histograms
Image enhancement
Image quality
Feature fusion
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