Key Technology of the Medical Image Wise Mining Method Based on the Meanshift Algorithm

Cui, Jinli; Wang, Yadong; Wang, Ke

doi:10.1155/2022/6711043

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…In this paper, we used the Meanshift algorithm to extract single-tree point clouds. Meanshift is a density-based clustering algorithm that assumes that data sets of different clusters conform to different probability density distributions, and regions with high sample density correspond to the centers of clusters [41]. Therefore, when segmenting two slightly connected tree point clouds, the algorithm determines the clustering center based on the density of the point cloud and performs clustering.…”

Section: Clustering Extraction Of Point Cloudsmentioning

confidence: 99%

Study on Single-Tree Extraction Method for Complex RGB Point Cloud Scenes

Xia

Yang

et al. 2023

Remote Sensing

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With the development of sensor technology and point cloud generation techniques, there has been an increasing amount of high-quality forest RGB point cloud data. However, popular clustering-based point cloud segmentation methods are usually only suitable for pure forest scenes and not ideal for scenes with multiple ground features or complex terrain. Therefore, this study proposes a single-tree point cloud extraction method that combines deep semantic segmentation and clustering. This method first uses a deep semantic segmentation network, Improved-RandLA-Net, which is developed based on RandLA-Net, to extract point clouds of specified tree species by adding an attention chain to improve the model’s ability to extract channel and spatial features. Subsequently, clustering is employed to extract single-tree point clouds from the segmented point clouds. The feasibility of the proposed method was verified in the Gingko site, the Lin’an Pecan site, and a Fraxinus excelsior site in a conference center. Finally, semantic segmentation was performed on three sample areas using pre- and postimproved RandLA-Net. The experiments demonstrate that Improved-RandLA-Net had significant improvements in Accuracy, Precision, Recall, and F1 score. At the same time, based on the semantic segmentation results of Improved-RandLA-Net, single-tree point clouds of three sample areas were extracted, and the final single-tree recognition rates for each sample area were 89.80%, 75.00%, and 95.39%, respectively. The results demonstrate that our proposed method can effectively extract single-tree point clouds in complex scenes.

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Section: Clustering Extraction Of Point Cloudsmentioning

confidence: 99%

Study on Single-Tree Extraction Method for Complex RGB Point Cloud Scenes

Xia

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”

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confidence: 99%