Robust Spatial Autoregressive Modeling for Hardwood Log Inspection
Robust Spatial Autoregressive Modeling for Hardwood Log Inspection
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发表时间：
1997
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通讯作者：
L. Ouis
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作者：
L. Ouis
我们探讨了随机纹理建模方法在林产工业原木检测机器视觉系统中的应用。该机器视觉系统使用计算机断层扫描（CT）成像来定位和识别硬木原木的内部缺陷。CT应用于此类工业视觉问题需要高效且鲁棒的图像分析方法。本文讨论了创建这样一个计算机视觉系统，即使用图像纹理建模木材缺陷识别的问题的一个特定方面。特别是，我们贡献的第一个应用程序的空间自回归（SAR）建模木材纹理分析的CT图像的硬木日志。为此，本文提出了一种基于圆位移相关的原木CT图像圆纹理识别方法。一个强大的算法参数估计，以获得与个别缺陷发生在日志内的模型参数。基于估计的模型特征，构造了一个简单的最小距离相关分类器，将未知缺陷分类为原型缺陷之一。从所提出的方法，适用于不同的红橡木的CT图像的实验结果，并显示我们的方法的有效性。© 1994
We explore the application of a stochastic texture modeling method toward a machine vision system for log inspection in the forest products industry. This machine vision system uses computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. The application of CT to such industrial vision problems requires efficient and robust image analysis methods. This paper addresses one particular aspect of the problem of creating such a computer vision system, namely, the use of image texture modeling for wood defect recognition. In particular, we contribute the first application of spatial autoregressive (SAR) modeling to wood-grain texture analysis of CT images of hardwood logs. Thereto a circularly shifted correlation approach is developed to discriminate the circular texture patterns on the cross-sectional CT images of logs. A robust algorithm for parameter estimation is applied to obtain model parameters associated with individual defects occurring inside a log. Based on the estimated model features, a simple minimum distance correlationclassifier is constructed which classifies an unknown defect into one of the prototypical defects. Experimental results from the proposed method, applied to CT images from different red oak wood, are given and show the efficacy of our approach. © 1994