[1]周熊,多化琼*,王振柱,等. 基于图像处理和SVM探索木材与树皮的新识别参数[J].西北林学院学报,2014,29(05):199-202.[doi:doi:10.3969/j.issn.1001-7461.2014.05.38]
　ZHOU Xiong,DUO Hua-qiong*,WANG Zhen-zhu,et al. New Recognition Parameters of Wood and Bark Rate Based on Image Processing and the SVM[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2014,29(05):199-202.[doi:doi:10.3969/j.issn.1001-7461.2014.05.38]
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 基于图像处理和SVM探索木材与树皮的新识别参数()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第29卷
期数:
2014年05期
页码:
199-202
栏目:
出版日期:
2014-09-30

文章信息/Info

Title:
 New Recognition Parameters of Wood and Bark Rate Based on Image Processing and the SVM
文章编号:
1001-7461(2014)05-0199-04
作者:
 周熊1; 多化琼1*; 王振柱1; 任拓2
 (1.内蒙古农业大学 材料科学与艺术设计学院,内蒙古 呼和浩特 010018;2.国电联合动力,江苏 连云港 222069)
Author(s):
 ZHOU Xiong1;  DUO Hua-qiong1*;  WANG Zhen-zhu1;  REN Tuo2
 (1.Department of Material Science and Art Design, Inner Mongolia Agricultural University, Hohhot, Inner Monggolia 010018, China; 2. Department of Technical, United Power Limited Company, Lianyungang, Jiangsu 222069, China)
关键词:
 ; 图像识别; 特征参数; 核函数
Keywords:
 ; image recognition;  characteristic parameter;  kernel function
分类号:
S781.1
DOI:
doi:10.3969/j.issn.1001-7461.2014.05.38
文献标志码:
A
摘要:
 木材工业生产中，需将树皮和木材分离，进而提高木材的使用效率。运用木材和树皮的5个特征参数和数字图像处理技术对榆木(光秃大果榆Ulmus macrocarpa var. glabra)、柳木(杞柳Salix integra)和松木(樟子松Pinus sylvestris var. mongolica)的木材与树皮图像进行分类识别，其中均方差比是本文提出的识别参数。通过对图片进行数字图像处理，得出参数的最大值和最小值, 利用多项式函数和非线性函数对木材和树皮识别，再对参数进行评估，筛选出最佳参数。结果表明，参数均方差比识别率最高分别达到97.7%和 94.7%,且多项式函数的识别效果高于非线性函数的识别效果。
Abstract:
 In the industrial production of wood, bark and wood must be separated in order to improving the efficiency of the use of wood. In this paper, five characteristic parameters of wood and bark and digital image processing technology were used to recognize wood and bark of the elm, willow, and Pine. The mean square error ratio was selected as the identification parameter. The maximum and minimum values of the parameter were obtained by processing the images digitally. Wood and bark were recognized by using the polynomial function (PF) and a nonlinear function (NLF). Parameters obtained were evaluated to select the best parameters. The results showed that by using mean square error as parameter, highest rates of recognition were achieved: 97% (PF) and 94.7% (NLF).

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备注/Memo

备注/Memo:
 收稿日期：2013-12-22修回日期：2014-02-15
基金项目：国家自然科学基金项目（30960303）；内蒙古自治区自然科学基金项目（2013MS0529）。
作者简介：周熊，男，硕士，研究方向：图像处理技术在木材科学中的应用。E-mail：443732308@qq.com
*通信作者：多化琼，女，教授，研究方向：材料科学与艺术设计。E-mail：duohuaqiong@163.com
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