Research on point cloud segmentation method of mutually adhered cherry tomatoes based on three-dimensional data

Zuwei Zhao; Bo Li; Zhengtao Zhu

doi:10.1117/12.3039086

13 September 2024 Research on point cloud segmentation method of mutually adhered cherry tomatoes based on three-dimensional data

Zuwei Zhao, Bo Li, Zhengtao Zhu

Author Affiliations +

Proceedings Volume 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024); 1325418 (2024) https://doi.org/10.1117/12.3039086
Event: Fourth International Conference on Optics and Image Processing (ICOIP 2024), 2024, Chongqing, China

Abstract

In order to improve the accuracy of recognition and segmentation of occluded and stuck cherry tomatoes in three-dimensional infrared images, an improved watershed segmentation method based on point cloud spatial density cluster analysis is proposed. First, a binocular infrared camera is used to collect a three-dimensional point cloud image, and then the point cloud holes are filled with geometric diffusion. The completed point cloud model is passed through a color region growing segmentation algorithm to extract the fruit bunch target. The phenotypic characteristics of the target are analyzed through point cloud density clustering, and then the concave-convex relationship is used to enhance the edge characteristics of the fruit. Finally, secondary clustering is performed based on the watershed idea to separate the blocked and adherent fruits. Experimental results show that the algorithm has a recall rate of 88.9% in target recognition of stuck cherry tomatoes, which improves the three-dimensional segmentation performance of stuck cherry tomatoes.

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

Citation Download Citation

Zuwei Zhao, Bo Li, and Zhengtao Zhu "Research on point cloud segmentation method of mutually adhered cherry tomatoes based on three-dimensional data", Proc. SPIE 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024), 1325418 (13 September 2024); https://doi.org/10.1117/12.3039086

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