基于纹理谱的纹理分割方法

纹理分析是图象处理中的一个重要领域。本文提出一种基于纹理谱特征分割纹理图象的方法。它首次将纹理谱特征与区域生长算法结合起来，从而实现了无监督的纹理分割。纹理谱特征具有对方向性敏感等优点，基于纹理谱的纹理图象分割取得了良好效果。     

基于纹理梯度的文档图像的倾斜校正方法

文档图像的倾斜校正在光学字符识别以及文档理解系统研究中有着重要的意义,国内外学者提出了很多实现方法,但各种方法都存在一定的局限性.通过对基于Hough变换和投影的倾斜校正方法的分析,提出了一种基于文档图像纹理方向的倾斜校正方法:文档图像中的文本纹理整体表现出一定的方向性,使文本图像能保持水平,通过纹理方向性分析,找出纹理的主要方向,进而求得文档的倾斜角度.通过一个复杂版面的二值文档图像的检测校正实验表明,方法提高了倾斜校正的校正范围,而且具有较好的有效性和鲁棒性.     

CrackTree: Automatic crack detection from pavement images

Pavement cracks are important information for evaluating the road condition and conducting the necessary road maintenance. In this paper, we develop CrackTree, a fully-automatic method to detect cracks from pavement images. In practice, crack detection is a very challenging problem because of (1) low contrast between cracks and the surrounding pavement, (2) intensity inhomogeneity along the cracks, and (3) possible shadows with similar intensity to the cracks. To address these problems, the proposed method consists of three steps. First, we develop a geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks. Second, we build a crack probability map using tensor voting, which enhances the connection of the crack fragments with good proximity and curve continuity. Finally, we sample a set of crack seeds from the crack probability map, represent these seeds by a graph model, derive minimum spanning trees from this graph, and conduct recursive tree-edge pruning to identify desirable cracks. We evaluate the proposed method on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods.     

基于纹理分析的表面粗糙度等级识别

提出了一种利用图象纹理分析技术进行机械加工表面粗糙度检测的非接触检测方法,该方法首先根据统计方差对待测工件的表面粗糙度进行粗分类,然后,利用基于Ｇａｂｏｒ滤波器的纹理分类器,识别待测工件表面粗糙度等级。该新方法可简单、快速地实现表面粗糙度等级的自动识别,而且对图象旋转具有不变性,由于其纹理分类器的参数少,并且新方法成本低,参数标定方便,因而便于现场检测,如果与机床的控制系统相连,还可以实现加工的实     

Wavelet based methods on patterned fabric defect detection

The wavelet transform (WT) has been developed over 20 years and successfully applied in defect detection on plain (unpatterned) fabric. This paper is on the use of the wavelet transform to develop an automated visual inspection method for defect detection on patterned fabric. A method called direct thresholding (DT) based on WT detailed subimages has been developed. The golden image subtraction method (GIS) is also introduced. GIS is an efficient and fast method, which can segment out the defective regions on patterned fabric effectively. In this paper, the method of wavelet preprocessed golden image subtraction (WGIS) has been developed for defect detection on patterned fabric or repetitive patterned texture. This paper also presents a comparison of the three methods. It can be concluded that the WGIS method provides the best detection result. The overall detection success rate is 96.7% with 30 defect-free images and 30 defective patterned images for one common kind of patterned Jacquard fabric.     

A cue generator for crack detection

This paper describes a computationally efficient method for identifying weakly visible cracks in images of man-made surfaces. The method relies on a multistage nonlinear filtering process designed to yield maximum response to symmetrical fine structure. The result is a set of candidate sites which can then be verified on the basis of features obtained from the source image and candidate shape. Results are presented which demonstrate the application of the method to a difficult problem in industrial inspection.     

Suitability Analysis of Techniques for Flaw Detection in Textiles using Texture Analysis

This paper addresses issues conerning image analysis algorithms for the visual quality inspection of textile fabrics. An overview of a number of flaw detection techiques and analysis of their suitability for detecting the presence of weaving defects is presented. The flaw detection algorithms are based on a local analysis of spatial and spatial-frequency domain features, exploring both the statisical and structural texture analysis of the defective image samples. The algorithms are compared using two criteris: accuracy and computational efficiency. Four methods are applied to the flaw detection problem, and their accuracy is assessed experimentally on a number of common textile defects in this preliminary comparison. The methods are: spatial grey level co-occurrence; normalised cross-correlation; texture blob detection; and spectral approeaches. Of these mehods, the correlation approch appears to be the most promising candidate for a real-time, high accuracy flaw detection algorithm. Received: 21 December 1998, Received in revised form: 03 September 1999, Accepted: 03 September 1999     

Independent component analysis-based defect detection in patterned liquid crystal display surfaces

In this paper, we propose a machine vision approach for automatic detection of micro defects in periodically patterned surfaces and, especially, aim at thin film transistor liquid crystal display (TFT-LCD) panels. The proposed method is based on an image reconstruction scheme using independent component analysis (ICA). ICA is first applied to a faultless training image to determine the de-mixing matrix and the corresponding independent components (ICs). The ICs representing the global structure of the training image are then identified and the associated row vectors of those ICs in the de-mixing matrix are replaced with a de-mixing row representing the least structured region of the training image. The reformed de-mixing matrix is then used to reconstruct the TFT-LCD image under inspection. The resulting image can effectively remove the global structural pattern and preserve only local anomalies. A number of micro defects in different TFT-LCD panel surfaces are evaluated with the proposed method. The experiments show that the proposed method can well detect various ill-defined defects in periodically patterned surfaces.     

结合双注意力机制的道路裂缝检测

目的 道路裂缝检测旨在识别和定位裂缝对象,是保障道路安全的关键问题之一。为解决传统深度神经网络在检测背景较复杂、干扰较大的裂缝图像时精度较低的问题,设计了一种基于双注意力机制的深度学习道路裂缝检测网络。方法 本文提出了在骨干网络中融入空洞卷积和两种注意力机制的方法,将其中的轻量型注意力机制与残差模块结合为残差注意力模块Res-A。对比研究了该模块“串联”和“并联”两种方式对于裂缝特征关系权重的影响并获得最佳连接。同时,引入Non-Local计算模式的注意力机制,通过挖掘特征图谱的关系权重以提高裂缝检测性能。结合两种注意力机制可以有效解决复杂背景下道路裂缝难检测的问题,提高了道路裂缝检测精度。结果 在公开复杂道路裂缝数据集Crack500上进行对比实验与验证。为证明本文网络的有效性,将平均交并比(mean intersection over union, m Io U)、像素精确度(pixel accuracy, PA)和训练迭代时间作为评价指标,并进行了3组对比实验。第1组实验用于评价残差注意力模块中通道注意力机制和空间注意力机制之间不同组合方式的检测性能,结果表明这两种机制并联相加时...     

An image processing-based crack detection technique for pressed panel products

Crack detection is an important step in assessing the quality of pressed panel products. This paper presents a fast and non-invasive crack detection technique which involves extracting the outline of the captured object and applying a unique edge line evaluation. This technique is robust against environmental condition changes and only require a low-cost web camera. After capturing an image immediately following the press process, a clear one-pixel edge line is extracted by applying a light control and a series of pre-image processing algorithms, including a valley-emphasis Otsu method and percolation-based shape recognition. Next, the initial detection at low resolution is applied to search for every possible crack using unique edge line and curvature evaluation. Finally, at high resolution, the windowed image of every possible crack is individually analyzed to detect existing cracks using a more specific evaluation process. All of these steps are completed within 0.5 s, thus allowing for the technique to be applied in real-time on a highly automated manufacturing line. To demonstrate the performance of the proposed technique, experiments are conducted on an aluminum plate with different patterns and the pressed panel products. The results show that the proposed technique can detect surface cracks on pressed panels with stable performance as well as high accuracy and efficiency.     

基于遗传神经网络的机场跑道局部影响的裂缝检测算法

针对机场跑道裂缝的自主识别和提取过程中存在的阴影、光照不均匀以及效率和精度难以兼顾等一系列问题,提出利用遗传算法优化神经网络的机场道面裂缝检测算法。首先,将拍摄的机场道面裂缝图像进行预处理,包括图像灰度化、高斯滤波以及ROI区域确定。设定神经网络拓扑结构,初始化编码长度以权值阈值及等参数,利用选择、交叉和变异等操作反复执行至最佳进化解,进而搭建匹配的神经网络,获得最大分割阈值。结果表明,遗传神经网络算法在综合评价、召回率、和准确率3个评价指标上均具有显著提升,其均值分别为93.22%、96.28%、90.75%,实现了在复杂背景下对裂缝提取的目标,为机场道面的后期维护和保养提供了技术支持。     

CrackEmbed: Point feature embedding for crack segmentation from disaster site point clouds with anomaly detection

Laser-scanned point clouds can be used to represent the 3D as-damaged condition of building structures in a post-disaster scenario. Performing crack detection from the acquired point clouds is a critical component of disaster relief tasks such as structural damage assessment and risk assessment. Crack detection methods based on intensity or normals commonly result in noisy detections. On the other hand, deep learning methods can achieve higher accuracy but require a large dataset of annotated cracks. This research proposes an unsupervised learning framework based on anomaly detection to segment out cracked regions from disaster site point clouds. First, building components of interest are extracted from the point cloud scene using region growing segmentation. Next, a point-based deep neural network is used to extract discriminative point features using the geometry of the local point neighborhood. The neural network embedding, CrackEmbed, is trained using the triplet loss function on the S3DIS dataset. Then, an anomaly detection algorithm is used to separate out the points belonging to cracked regions based on the distribution of these point features. The proposed method was evaluated on laser-scanned point clouds from the 2015 Nepal earthquake as well as a disaster response training facility in the U.S. Evaluation results based on the point-level precision and recall metrics showed that CrackEmbed in conjunction with the isolation forest algorithm resulted in the best performance overall.     

An independent component analysis-based filter design for defect detection in low-contrast surface images

In this paper, we propose a convolution filtering scheme for detecting small defects in low-contrast uniform surface images and, especially, focus on the applications for backlight panels and glass substrates found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and even worse, the sensed image may present uneven brightness on the surface. All these make the defect detection in low-contrast surface images extremely difficult.In this study, a constrained independent component analysis (ICA) model is proposed to design an optimal filter with the objective that the convolution filter will generate the most representative source intensity of the background surface without noise. The prior constraint incorporated in the ICA model confines the source values of all training image patches of a defect-free image within a small interval of control limits. In the inspection process, the same control parameter used in the constraint is also applied to set up the thresholds that make impulse responses of all pixels in faultless regions within the control limits, and those in defective regions outside the control limits. A stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to solve for the constrained ICA model. Experimental results have shown that the proposed method can effectively detect small defects in low-contrast backlight panels and LCD glass substrate images.     

基于运行时纹理合成的纹理画刷

纹理画刷是一种交互式纹理生成工具,能够在用户控制下生成所需要的纹理.本文提出了一种基于运行时纹理合成的纹理画刷实现算法.纹理合成采用逐块合成的方式,每次将整个样本图以选定的位移放置到合成图中,然后用Graph Cut来决定最终的输出区域.在预处理过程中计算得到两个相同样本图间的最佳切合位移集合,在合成过程中,贴块位移搜索范围限定在由此集合及用户控制所决定的一个很小的范围内,使合成速度达到实时.另外通过对"孤立块"采取"虚拟贴块法"选取贴块位移,较好地保持了画刷所生成纹理的一致性.实验结果表明该纹理画刷的纹理生成速度满足与用户交互的需求,且生成纹理的质量高.     

基于先验分布活动轮廓模型的纹理缺陷检测

在工业产品的表面缺陷检测中,计算机视觉逐渐取代人工视觉,这是工业自动化的重要标志之一.而产品的表面纹理对缺陷检测的干扰一直是个难点.从图像分割的角度出发,以缺陷为目标,将纹理表面作为背景提取产品的表面缺陷.基于非参数统计活动轮廓模型提出一种先验分布模型,即以纹理的灰度分布作为背景的先验信息,使得算法更容易区分纹理背景和缺陷.实验结果表明,所提出的算法适用于不同纹理背景的缺陷检测,能准确地提取缺陷位置.     

一类新的纹理谱描述子

在纹理元的基础上提出了一类新的纹理谱描述子,新的纹理谱描述子在3个方面作了改进：将像素的灰度差量化为4个值;量化区间根据纹理对比度自动确定,并保证量化值具有灰度线性不变性;利用相关性弱的8邻域像素构建纹理谱描述子,从而降低了纹理谱维数。定义了基于新的纹理谱描述子的光照、旋转不变性纹理特征。利用该特征对Outex纹理进行光照、旋转不变性分类,分类准确率高于基于局部二值模式的光照、旋转不变性纹理特征。     

Defect detection of uneven brightness in low-contrast images using basis image representation

In this paper, we propose a machine vision approach for detecting local irregular brightness in low-contrast surface images and, especially, focus on mura (brightness non-uniformity) defects in liquid crystal display (LCD) panels. A mura defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and even worse, the sensed image may also present uneven illumination on the surface. All these make the mura defect detection in low-contrast surface images extremely difficult.A set of basis images derived from defect-free surface images are used to represent the general appearance of a clear surface. An image to be inspected is then constructed as a linear combination of the basis images, and the coefficients of the combination form the feature vector for discriminating mura defects from clear surfaces. In order to find minimum number of basis images for efficient and effective representation, the basis images are designed such that they are both statistically independent and spatially exclusive. An independent component analysis-based model that finds both the maximum negentropy for statistical independency and minimum spatial correlation for spatial redundancy is proposed to extract the representative basis images. Experimental results have shown that the proposed method can effectively detect various mura defects in low-contrast LCD panel images.     

基于OpenGL的复杂曲面的纹理映射

介绍利用OpenGL和Visual C 6.0进行复杂曲面的纹理映射。利用解二次随圆偏微分方程的方法，得到任意曲面到平面的共形映射。此方法可以自动分配纹理坐标到复杂的没有起伏的曲面，有效地克服复杂曲面的自动纹理映射的变形，避免了纹理扰动。