[1]鄂雪,程朋乐*. 基于改进归一化割的立木边缘识别技术研究[J].西北林学院学报,2018,33(3):232-237.[doi:10.3969/j.issn.1001-7461.2018.03.36]
 E Xue,CHENG Peng-le*. Edge Recognition Technology of Livieng Trees Based on Improved Normalized Cut[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2018,33(3):232-237.[doi:10.3969/j.issn.1001-7461.2018.03.36]
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 基于改进归一化割的立木边缘识别技术研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第33卷
期数:
2018年第3期
页码:
232-237
栏目:
出版日期:
2018-05-31

文章信息/Info

Title:
 Edge Recognition Technology of Livieng Trees Based on Improved Normalized Cut
文章编号:
1001-7461(2018)03-0232-06
作者:
 鄂雪程朋乐*
 (北京林业大学 工学院,北京 100083)
Author(s):
 E XueCHENG Peng-le*
 (College of Engineering,Beijing Forestry University,Beijing 100083,China)
关键词:
 立木图像权值矩阵归一化边缘识别
Keywords:
 tree image weight matrix normalization edge recognition
分类号:
S758.1
DOI:
10.3969/j.issn.1001-7461.2018.03.36
文献标志码:
A
摘要:
 立木胸径是评价林木生长状况的重要依据,采用机器视觉的检测方法,实现立木胸径的自动测量。为了快速准确地识别立木边缘,根据立木图像的特点,提出特定方向的归一化分割方法。预处理采用具有保边去噪功能的双边滤波器对立木图像进行滤波,用局部均值法进行一定的像素减少处理。本研究重点是在传统权值矩阵的基础上构造像素点满足特定斜率要求的权值矩阵,两点斜率在45°~135° 2个像素点间的权值为1,在此范围之外的权值为0,构造无向带权图后进行归一化分割。最后利用此算法对30株立木图片在MATLAB 2015环境下进行仿真试验,结果显示立木边缘的平均误提取率为1.21%,小于森林资源清查1.5%的误差要求。
Abstract:
 The DBH is an important basis for evaluating the growth status of trees.In this paper,the automatic measurement of the DBH was achieved by the method of machine vision detection.In order to quickly and accurately identify the edge of the wood,according to the characteristics of the images,this paper presented a specific direction of the normalized segmentation method.Preprocessing used a bilateral filter with edge preserving function to filter the image of the wood,and used the local mean method to perform certain pixel reduction processing.We focused on constructing the weight matrix of pixels to satisfy the specific slope requirement on the basis of the traditional weight matrix.The slope of two pixels was 1 in the specified range,and the weight outside this range was 0,an undirected weighted graph was constructed and the segmentation was normalized.Finally,the algorithm was used to simulate 30 trees in MATLAB 2015 environment.The results showed that the average error rate was 1.21% on the edge of the standing tree,1.5% less than the requirements on forest inventory.

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备注/Memo

备注/Memo:
 收稿日期:2017-05-20修回日期:2017-11-23
基金项目:国家自然科学基金(31200431)。
作者简介:鄂雪,女,硕士,研究方向:机器视觉技术。E-mail:1804391876@qq.com
*通信作者:程朋乐,男,博士,副教授,硕士生导师,研究方向:林业信息测量技术。E-mail:chengpengle@bjfu.edu.cn
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