摘要
木材识别是根据木材构造、材色、纹理与气味等特征确定木材树种名称的工作。在这些识别特征中,宏观和微观的木材构造特征是进行木材识别的主要依据。而正确的木材识别对于木材科学发展、木材资源的合理使用与管理、木材贸易流通、木材进出口管理和木材考古等等都有重要的意义。本文从木材的语义特征和纹理特征两个方面来对基于木材的宏观和微观图像的木材识别方法进行了研究。具体来说,全文的主要工作概括如下：
(1)采集华东地区及主要进口树种木材,构建了包括木材标本的树种名称、产地、构造、性质、用途、宏观和微观图像等内容的数据库,建立了木材标本馆信息管理系统,实现按木材名称、木材各类属性进行查询检索。这为木材相关专业和行业的研究、教学、生产、经营贸易等提供服务,为正确认识和了解各种木材的性质,更合理、高效地利用木材资源提供可靠的数据依据。目前已经收集500余种木材图像信息及其各种物理、化学等特性数据。同时选择了一些典型的木材进行了体视图像拍摄,以及制作木材切片后的显微图像拍摄。这些图片经图像预处理后,分别形成了两个数据集：ZAFU WS24和ZAFU WM22.并以这两个数据集为样本,验证本文所提出的木材识别方法的有效性。
(2)提出了一种采用混合型水平集方法进行木材显微图像分割的方法,通过引入局部图像信息降低木材显微图像由于局部不均匀而造成的分割困难,并利用一种基于类圆区域的面积直方图方法,自动计算封闭区域最佳阈值。通过该面积阈值,可解决水泡等杂质问题。然后,根据木材学领域知识,引入与封闭区域平均面积参数作为第二个目标函数,将单目标变成多目标问题。实验结果显示采用多目标函数后对不同种类的阔叶树材均能获得满意的导管分割效果。最后根据获得的导管,采用管孔外接圆与周围相邻区域判断它们的邻接关系,然后根据管孔的邻接度来判断管孔的组合方式。分割实验显示得到的管孔组合方式与实际的基本一致。
(3)基于高阶局部自相关(Higher order Local Auto-Correlation, HLAC)概念,提出了一种新的Mask Matching Image (MMI)方法,弥补目前HLAC及其扩展方法的不足,通过计算HLAC的MMI,保留了模板下的图像的所有信息。在MMI上,不仅可以得到局部统计特征,我们还提出了长度直方图(LH)特征。这种特征能有效地提取出图像整体的几何结构特征信息,从而最终能够有效地对图像进行纹理分析。另外,我们提出的最大最小排序局部自相关特征(Max-Min Sorted HLAC, MMS HLAC)通过排序能够简单、快速地生成有效特征,避免了原HLAC类方法计算特征值面临的模板面积累加和过大的问题。实验结果显示,MMI和MMS HLAC特征都表现出较高的识别效果。
(4)本文提出基于块改进Gabor小波和Greedy Sort Search (GSS)特征选择算法对木材体视图进行了自动分类研究。首先通过Gabor小波提取木材纹理特征,分析了最佳尺度和方向参数的选取后,根据木材体视图图像的特点,在Gabor滤波组上采用合适的分块方式,除了均值和标准差外,还引入熵、对比度等统计量提取更多的有效特征,并提出Greedy Sort Search (GSS)的查找最优子集的搜索方法进行降维,从而获得了区分能力最强的分类特征。对木材进行分类实验结果表明,我们提出的方法能够很好地改进木材的识别率和运行效率。
Wood recognition is a work involoved in how to identify the names of wood species, which is based on wood structure, color, texture, smell and other characteristics. In these wood classification features, wood recognition is mainly relied on the macroscopic and microscopic characteristics of wood. As we know, the correct recognition of wood is an important task to wood science development, reasonable use and management of timber resources, timber trade circulation, timber import and export management, and wood archaeology, etc. This dissertation conducts the research on the wood recognition based on the macroscopic and microscopic images of woods from the two aspects of the semantic features and texture features of woods. Specifically, the main works for this dissertation are summarized as follows:
(1) We collected the wood samples from the main import wood species in East China regions, and constructed the database which contains the wood species name, origin, structures, properties, uses, macroscopic and microscopic images of wood specimen; then, established the web based wood herbarium information management system, which can research and retrieve the corresponding information by wood name and wood properties. To supply the services for research, teaching, production and business trade of related wood profession and industry, this information management system provides reliable data for correctly recognizing and understanding the nature of a variety of wood, and for taking more rational and efficient use of the timber resources. At present, we have collected more than500kinds of wood images, where the extracted information for their various application properties include physical, chemical, etc. At the same time, we selected some typical woods to obtain their stereogram images, and take the microcosmic images from the wood pieces after wood cut-section has been made. After pretreating these images, these pictures respectively form two data sets:ZAFU WS24and ZAFU W M22. We took these two datasets as the samples to verify the effectiveness of the proposed wood recognition methods.
(2) We proposed a segmentation method for wood microscopic image, which is used a mixed level sets. Through introducing the local image information, we reduced the segmentation difficulty of wood microscopic image caused by the local uneven sections in the image. And with the used area histogram based on the circular regions, we can obtain the best threshold parameter for the closed area computation automatically. By using the area threshold, we can solve the impurities problems in the wood images, such as blisters. Then, according to the domain knowledge of wood science, the second objective function is introduced, which is the average area, a parameter about all the closed area in wood image, so the image segmentation task converts the one from the single object problem to a multi-objective one. The corresponding experimental results show that we can acquire a satisfying segmentation result under the different circumstances of many kinds of hardwood by using multi-objective function. Finally, according to the pores that we have got in the last step, we can judge their adjacencies by the circumcircle of pores and its neighboring regions; then, the adjacency degrees can be used to determine the combination of the pores. The combination mode of the pores, which we got from our segmentation experiments, is consistent with practical.
(3) Based on higher order local auto-correlation (HLAC), we proposed a new concept:Mask Matching Image (MMI), to make up the deficiencies of HLAC and its expansion methods. This method can retain all the information about the template image by calculating the MMI of HLAC. From the MMI of images, we can not only obtain the local statistical characteristc, but also the geometrical characteristc information about the whole images, obtained effectively by the length histogram (LH) feature we proposed, which can be taken as a useful tool for the texture analysis of image. In addition, Max-Min Sorted HLAC (MMS HLAC) we proposed can be used to generate the features simply, quickly, and effectivly by sorting, and at the same time, avoid the problem of the template cumulative sum too big when the previouse HLAC based methods are used to calculate their features. The experimental results show that MMI and MMS HLAC can obtain a higher recognition rate in our wood datasets.
(4) This dissertation proposed a method based on the modified blocked Gabor wavelet and Greedy Sort Search (GSS) feature selection algorithm, to conduct the investigation on the automatic wood classification by wood stereogram images. Firstly, we used Gabor wavelet to extract the texture features of wood. After analyzing the best parameters selection of the scale and orientation, we took a suitable means of partitioning on Gabor filter group according to the characteristics of wood stereography. In addition to the mean and standard deviation, more statistics features including entropy, contrast and other statistics are used to extract more effective features. Moreover, the search method for how to find the best subset, which is called Greedy Sorting Search (GSS), is used to reduce the feature dimension. So we can obtain the classification features which have the best capability of distinguishing different woods. The experimental results of wood classification show that the method we proposed can well improve the wood recognition rate and its working efficiency.
引文
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