共查询到20条相似文献，搜索用时 265 毫秒
1.
针对传统区域生长方法识别实木地板节子存在准确率低且速度慢的问题,运用TRIZ中矛盾解决理论分析与物质——场分析,提出一种结合分水岭、区域生长以及边缘检测的新的实木地板节子识别算法。算法首先将原图像转换为灰度图像;其次,运用形态学分水岭的方法对灰度图像进行分割;再次,选取满足条件的种子区域进行区域生长,得到节子区域;最后,运用Sobel算子对图像进行梯度运算,并找到节子的边缘。仿真实验表明,该算法较传统方法能够找到更合适的种子区域和区域生长的阈值,实现了对节子的快速、完整提取,节子分割平均用时60ms,平均辨识准确率在90％以上。 相似文献
2.
An automated wood texture recognition system of 48 tropical wood species is presented. For each wood species, 100 macroscopic texture images are captured from different timber logs where 70 images are used for training while 30 images are used for testing. In this work, a fuzzy pre-classifier is used to complement a set of support vector machines (SVM) to manage the large wood database and classify the wood species efficiently. Given a test image, a set of texture pore features is extracted from the image and used as inputs to a fuzzy pre-classifier which assigns it to one of the four broad categories. Then, another set of texture features is extracted from the image and used with the SVM dedicated to the selected category to further classify the test image to a particular wood species. The advantage of dividing the database into four smaller databases is that when a new wood species is added into the system, only the SVM classifier of one of the four databases needs to be retrained instead of those of the entire database. This shortens the training time and emulates the experts’ reasoning when expanding the wood database. The results show that the proposed model is more robust as the size of wood database is increased. 相似文献
3.
The feasibility of identifying internal wood characteristics in computed tomography (CT) images of black spruce was investigated
using two promising classifiers: the maximum likelihood classifier (MLC) and the back propagation (BP) artificial neural network
(ANN) classifier. Nine image features including one spectral feature (gray level values), a distance feature, and seven textural
features were employed to develop the classifiers. The selected internal wood characteristics to be identified included heartwood,
sapwood, bark, and knots. Twenty cross-sectional CT images of a black spruce log were randomly selected to develop the two
classifiers. The results suggest that both classifiers produced high classification accuracy. Compared with the MLC classifier
(80.9% overall accuracy), the BP ANN classifier had better classification performance (97.6% overall accuracy). Moreover,
statistical analysis reveals that the heartwood of the black spruce log used in this study is the easiest to identify by either
classifier compared with the other three log features. The results also suggest that the separability of one wood characteristic
from the other wood characteristics in black spruce CT images is mainly related to moisture content. 相似文献
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[目的]以甘肃省天水市为例,基于遥感影像变化监测技术,探讨黄土高原丘陵沟壑与小陇山-西秦岭山地交接过渡区域近30年来森林(林地)资源空间分布规律、时间变化趋势及变化影响因素。[方法]以1988—2015年5期夏季Landsat TM/OLI遥感影像为主要数据源,结合辅助数据和外业实地样本点,以光谱特征和指数特征为特征变量,分别利用随机森林(RF)和参数优化支持向量机(POSVM)分类器对土地覆盖类型进行分类,然后基于分类后比较法进行森林资源动态变化监测。[结果]分类结果表明,两种分类器的分类效果均较好,且随机森林分类器在分类精度、效率和稳定性方面明显优于参数优化支持向量机分类器。变化监测结果表明,近30年来森林资源总体变化趋势为林地面积先减少后增加。1990—1996年,林地面积减少0.74%;1996—2002年,林地面积减少2.74%;2002—2008年,林地面积增加1.06%;2008—2015年,林地面积增加8.89%。[结论]本研究采用的基于非参数分类器分类后比较法的变化监测技术是复杂地形地貌过渡区森林资源动态变化监测的一种有效途径,在分类结果分析统计的基础上,得出研究区森林资源变化的总体趋势:以2002年(2002年影像)为界,林地总体趋势为先减少后增加,2002年后林地面积增加显著。 相似文献
6.
Soft measurement of wood defects based on LDA feature fusion and compressed sensor images 总被引：2，自引：0，他引：2
《林业研究》2017,(6)
We proposed a detection method for wood defects based on linear discriminant analysis(LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera,and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%. 相似文献
7.
为提高木质粉尘火花检测的准确性,利用基于支持向量机(SVM)的物质分类方法检测木质粉尘火花。选取马尾松和杨木粉尘为研究对象,将两种粉尘分组点燃试验,获取火花和灰分的高光谱图像,提取感兴趣区域(region of interest,ROI)内的发光度数据进行预处理。利用感兴趣区域内的数据建立SVM分类模型,分别利用网格搜索法(GS)、遗传算法(GA)以及粒子群算法(PSO)对两类树种的SVM分类模型进行参数优化,并将三种参数优选方法的分类预测准确率进行对比。结果表明,三种优化方法均能够很好地检测两种树种的木粉火花,其中网格搜索法检测准确率明显高于其余两种,更适于木质粉尘火花探测,这为人造板生产过程中能够高效检测木质粉尘火花提供了一定的理论依据。 相似文献
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利用阿达玛变换近红外光谱结合支持向量机,对制浆造纸常用木材树种的快速识别进行研究。将各树种近红外光谱先进行多点平滑和标准正态变换预处理以消除噪音干扰和光散射导致的测量偏差,然后基于不同建模策略建立一对多和一对一两种支持向量机模型,考察这两种模型对多树种属间分类和种间分类的预测能力,并与传统的偏最小二乘判别分析分类法进行对比。结果表明,支持向量机预测模型对桉木、相思木、杨木、水杉等树种的属间分类正确率达到98%以上,种间分类正确率均达到95%以上,在处理复杂分类问题时模型稳健性明显优于传统分类方法,从方法上证明了近红外技术工业化应用的可能性,为进一步建立近红外在线检测木片材性分析系统奠定了基础。 相似文献
10.
Núbia Rosa da Silva Maaike De Ridder Jan M. Baetens Jan Van den Bulcke Mélissa Rousseau Odemir Martinez Bruno Hans Beeckman Joris Van Acker Bernard De Baets 《Annals of Forest Science》2017,74(2):30
?Key message
Pattern recognition has become an important tool to aid in the identification and classification of timber species. In this context, the focus of this work is the classification of wood species using texture characteristics of transverse cross-section images obtained by microscopy. The results show that this approach is robust and promising.?Context
Considering the lack of automated methods for wood species classification, machine vision based on pattern recognition might offer a feasible and attractive solution because it is less dependent on expert knowledge, while existing databases containing high-quality microscopy images can be exploited.?Aims
This work focuses on the automated classification of 1221 micro-images originating from 77 commercial timber species from the Democratic Republic of Congo.?Methods
Microscopic images of transverse cross-sections of all wood species are taken in a standardized way using a magnification of 25 ×. The images are represented as texture feature vectors extracted using local phase quantization or local binary patterns and classified by a nearest neighbor classifier according to a triplet of labels (species, genus, family).?Results
The classification combining both local phase quantization and linear discriminant analysis results in an average success rate of approximately 88% at species level, 89% at genus level and 90% at family level. The success rate of the classification method is remarkably high. More than 50% of the species are never misclassified or only once. The success rate is increasing from the species, over the genus to the family level. Quite often, pattern recognition results can be explained anatomically. Species with a high success rate show diagnostic features in the images used, whereas species with a low success rate often have distinctive anatomical features at other microscopic magnifications or orientations than those used in our approach.?Conclusion
This work demonstrates the potential of a semi-automated classification by resorting to pattern recognition. Semi-automated systems like this could become valuable tools complementing conventional wood identification.11.
一种新的针叶材自动识别方法 总被引：1，自引：1，他引：0
提出通过横切面显微图像对针叶材树种进行计算机识别的方法。该方法通过提取图像的PCA特征,生成特征树,然后采用SVM对样本进行分类。使用8种针叶材,每种12个样本,并采用留一交叉验证,对图像的分割方法、最近邻与SVM分类算法和不同范数距离下的识别效果进行试验。结果表明通过部分木材微观的纹理结构进行木材识别的可能性。 相似文献
12.
13.
The knot depth ratio (KDR) evaluation method was designed to quantitatively evaluate the amount of knot in dimension lumber
by a single-pass X-ray radiation. To verify the proposed method, KDR values for 38-mm-thick specimens were predicted, and
they were compared with the actual measured KDR values. The knot is surrounded by the transition zone, and the density of
the knot and the transition zone is higher than the clear wood. Because the average density of the transition zone was similar
to the knot density, it was found that the proposed method gives the KDR values for the knot area including the transition
zone. The coefficients of determination between the predicted and measured KDR values were 0.87 and 0.83 for Japanese larch
and red pine specimens, respectively. Using the KDR information, the ratio of knot area including transition zone to cross-sectional
area was calculated. The presence of latewood and earlywood in the same path of the X-ray radiation caused discrepancies in
the estimation of KDR values because the density of latewood is much higher than that of earlywood. Fortunately, latewood
and earlywood are repeated in a cross section, so the amount of overestimation and underestimation was expected to be nearly
identical. As expected, the relationship between the predicted area ratio and the real area ratio of knot and transition zone
was strong with R
2 values of 0.89 and 0.93 for Japanese larch and red pine specimens, respectively. 相似文献
14.
谢永华 《林业机械与木工设备》2006,34(6):29-30
不变矩是模式识别中的一种重要方法,它具有平移不变性、比例不变性和旋转不变性等优点。本文将其引入到木材纹理的计算机视觉研究领域,提取了木材纹理的不变矩参数,并用提取的特征参数对木材纹理进行了分类研究,最近邻分类器的正确率为86.67%,获得了较高的分类正确率,从而验证了不变矩参数对木材纹理描述的有效性。 相似文献
15.
B. Koetz F. Morsdorf S. van der Linden T. Curt B. Allgwer 《Forest Ecology and Management》2008,256(3):263
Forest fire management practices are highly dependent on the proper monitoring of the spatial distribution of the natural and man-made fuel complexes at landscape level. Spatial patterns of fuel types as well as the three-dimensional structure and state of the vegetation are essential for the assessment and prediction of forest fire risk and fire behaviour. A combination of the two remote sensing systems, imaging spectrometry and light detection and ranging (LiDAR), is well suited to map fuel types and properties, especially within the complex wildland–urban interface. LiDAR observations sample the spatial information dimension providing explicit geometric information about the structure of the Earth's surface and super-imposed objects. Imaging spectrometry on the other hand samples the spectral dimension, which is sensitive for discrimination of surface types. As a non-parametric classifier support vector machines (SVM) are particularly well adapted to classify data of high dimensionality and from multiple sources as proposed in this work. The presented approach achieves an improved land cover mapping adapted to forest fire management needs. The map is based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR. 相似文献
16.
Javad Torkaman Mojgan Vaziri Dick Sandberg Soleiman Mohammadi Limaei 《Wood material science & engineering》2018,13(2):117-120
The classification of roundwood is inextricably linked to the measurement of a particular single wood defect. The appearance, location, and number of defects are important in the quality evaluation of logs and sawn timber, and the most important defects are knots. The purpose of this study was to investigate the relationship between the appearance of branch scars and features of the related knot inside oriental beech logs, and to model the relationship between well-defined branch-scar and knot parameters. One hundred and fifty knots in 15 stems of oriental beech trees were studied. Image analysis software was used to measure the branch-scar and knot features. The results showed a significant positive correlation between the branch-scar parameter “moustache length” and the knot length. The ratio of branch-seal length to width was found to be a good estimator of the stem diameter at the time of knot occlusion and the amount of clear wood between the knot occlusion and the bark. The relationship obtained for the oriental beech stem radius at time of knot occlusion confirms relationship reported for European beech (Fagus sylvatica L.). 相似文献
17.
以180幅木材样本图片为对象,研究以小波变换方法提取特征参数,分析几种小波基的特点和性质,最终以对称性为依据,选择使用sym4小波对图像进行二级小波分解,可以得到一级水平细节HL1、垂直细节LH1、对角细节HH1,二级的近似LL2、水平细节HL2、垂直细节LH2、对角细节HH2共7个子图,提取整幅图像的熵和每个子图小波系数的均值及标准差作为特征参数。将木材纹理按照直纹、抛物线和乱纹3种纹理的分类标准,以BP神经网络作为分类器进行了木材纹理分类的验证,并与灰度共生矩阵的方法进行了对比。试验表明:采用小波变换的方法对木材纹理特征进行描述,不但提高了分类的准确率,重要的是缩短了运算时间,可以达到在线监测的要求。 相似文献
18.
As an important material of making instrument resonant component, paulownia has a significant influence on instrument acoustic quality. Using the method of support vector machine (SVM), an evaluation model for predicting the Yueqin acoustic quality was developed based on the wood vibration performance. Generally, the wood selection in the Yueqin manufacture mainly depends on observance weighting by hands, knocking and listening by an instrument technician. The defect in scientific theory impedes the improvement of Yueqin quality. In this study, nine Yueqin were fabricated. Based on the information of their raw materials and Yueqin acoustic quality evaluation, a prediction model was proposed. In the total 180 groups of data, 60 groups of data were randomly selected for the training, 30 groups of data were randomly selected from the unused data for the verification. The radial basis function is used to establish the Yueqin soundboard wood acoustic quality evaluation model and simulate the prediction. The results revealed that the prediction of Yueqin acoustic quality could be achieved based on the soundboard wood vibration performance using the MATLAB simulation. The classification accuracy was 90.00%, indicating that the predicted values were highly consistent with the experimental values. The models are able to be used to precisely predict the Yueqin acoustic quality based on the vibration performance of soundboards. 相似文献
19.
基于Faster R-CNN的实木板材缺陷检测识别系统 总被引：1，自引：0，他引：1
我国木材资源有限,为了提高木材的利用率,采用机器视觉来实现木材缺陷快速而稳定的检测,不仅可以克服人工检测的低效率和木材缺陷识别的低准确率,而且对提高木材加工企业的智能化水平具有重要意义。为了高效、快速、准确地进行无损检测,采用深度学习方法,建立了一种基于快速深度神经网络的实木板材缺陷识别模型。首先采用Resnet V2结构对采集到的实木板材缺陷图像进行特征提取,然后应用该模型对节子、孔洞等实木板材缺陷进行训练学习,最后构建了Faster R-CNN检测框架,并使用tensorflow开发平台对节子、孔洞等实木板材缺陷进行预测输出。具体选取了2 000块杉木样本,通过旋转对原始的实木板材图像进行数据扩充,扩充后图像的80%作为训练集,20%作为验证集来进行仿真。仿真结果表明,该模型对实木板材节子缺陷检测正确率为98%,对实木板材孔洞缺陷检测正确率为95%,验证了将深度学习算法应用于实木板材缺陷检测中的有效性。 相似文献