| 基于改进K-means聚类与分水岭的木材横截面管孔分割 |
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| 引用本文： | 程昱之,钟丽辉,何鑫,王远,李朝岚.基于改进K-means聚类与分水岭的木材横截面管孔分割[J].浙江农林大学学报,2022,39(1):173-179. |
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| 作者姓名： | 程昱之 钟丽辉 何鑫 王远 李朝岚 |
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| 作者单位： | 1.西南林业大学 机械与交通学院，云南 昆明 6502242.西南林业大学 大数据与智能工程学院，云南 昆明 650224 |
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| 基金项目： | 云南省农业基础研究联合专项(2018FG001-108) |
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| 摘 要： | 目的 管孔是木材识别方面的重要特征之一。针对管孔随机分布、大小不一导致管孔分割鲁棒性不高，木纤维、木射线以及轴向薄壁组织等噪声区域对管孔分割效果影响较大的问题，本研究提出了一种改进K-means聚类与分水岭的木材横截面管孔分割算法。 方法 采用改进K-means聚类对管孔区域进行粗分割，有效区分管孔区域与木纤维、木射线以及轴向薄壁组织等噪声区域。再对粗分割结果采用改进分水岭算法进行精分割，分割出的管孔与实际管孔基本吻合。 结果 平均每张木材横截面微观图像有97.1%的管孔被准确有效地分割出来。本研究提出的改进分割算法与其他算法相比，分割效果显著提升，在大小不一且随机分布的管孔分割过程中鲁棒性高，具有良好的分割性能。 结论 该算法有效解决了传统K-means聚类算法在图像分割时受噪声影响大和初始聚类中心随机性问题，为阔叶材管孔特征提取和定量分析奠定了坚实基础。图7表1参16
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| 关 键 词： | K-means聚类 分水岭算法 管孔 图像分割 |
| 收稿时间： | 2021-03-11 |
Segmentation of wood cross-section pores based on improved K-means clustering and watershed |
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| CHENG Yuzhi,ZHONG Lihui,HE Xin,WANG Yuan,LI Chaolan.Segmentation of wood cross-section pores based on improved K-means clustering and watershed[J].Journal of Zhejiang A&F University,2022,39(1):173-179. |
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| Authors: | CHENG Yuzhi ZHONG Lihui HE Xin WANG Yuan LI Chaolan |
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| Affiliation: | 1.College of Machinery and Transportation, Southwest Forestry University, Kunming 650224, Yunnan, China2.College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, Yunnan, China |
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| Abstract: | Objective Pore is one of the important characteristics of wood identification. Aiming at the problems of poor robustness of pore segmentation due to random distribution and different sizes of pores and the impact of noise such as wood fiber, wood ray and axial parenchyma on the segmentation effect of pores, this study attempts to propose an improved K-means clustering and watershed algorithm for segmentation of wood cross-section pores. Method The improved K-means clustering was used to segment the pore area, which could distinguish the pore area from noise areas such as wood fiber, wood ray and axial parenchyma. Then, an improved watershed algorithm was used to segment the rough segmentation results, and the segmented pores were basically consistent with the actual pores. Result On average, 97.1% of the pores were segmented in each microscopic image of wood cross section. Compared with other algorithms, the improved algorithm had a significantly improved segmentation effect, high robustness and good segmentation performance in the process of pore segmentation with different sizes and random distribution. Conclusion This algorithm can effectively solve the problems of noise impact and randomness of initial clustering center in traditional K-means clustering algorithm in image segmentation, and lays a solid foundation for the feature extraction and quantitative analysis of hardwood pores. Ch, 7 fig. 1 tab. 16 ref.] |
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