基于傅里叶特征谱和相关系数的织物疵点检测

为提高织物疵点自动检测的准确度,提出一种基于傅里叶特征谱和相关系数的织物疵点检测算法。以平纹、斜纹织物为研究对象,对织物图像进行傅里叶变换,得到织物图像的频谱图;定位频谱中的特征峰点,提取表征图像灰度、纹理的五个特征值;以正常织物为模板,计算待检图像特征值与模板图像特征值之间的相关系数,确定用于识别织物疵点的阈值,来实现织物疵点检测。实验结果表明:当阈值设定为0.80时,该算法能够实现稀密路、断经、吊经、纬缩、破洞等常见疵点的准确检测。     

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.     

Defect detection in patterned wafers using anisotropic kernels

Wafer defect detection often relies on accurate image registration of source and reference images obtained from neighboring dies. Unfortunately, perfect registration is generally impossible, due to pattern variations between the source and reference images. In this paper, we propose a defect detection procedure, which avoids image registration and is robust to pattern variations. The proposed method is based on anisotropic kernel reconstruction of the source image using the reference image. The source and reference images are mapped into a feature space, where every feature with origin in the source image is estimated by a weighted sum of neighboring features from the reference image. The set of neighboring features is determined according to the spatial neighborhood in the original image space, and the weights are calculated from exponential distance similarity function. We show that features originating from defect regions are not reconstructible from the reference image, and hence can be identified. The performance of the proposed algorithm is evaluated and its advantage is demonstrated compared to using an anomaly detection algorithm.     

An object-oriented daytime land fog detection approach based on NDFI and fractal dimension using EOS/MODIS data

A new approach for land fog detection using daytime imagery from Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data based on the normalized difference fog index (NDFI) is proposed. NDFI is used to discriminate fog from clouds based on simulating and analysing the radiation characteristics of fog and cloud with MODIS data and the Streamer radiative transfer model. In this paper, in addition to the spectral and spatial characteristics of NDFI, the textural characteristics are introduced by using a fractal dimension. The fractal dimension is calculated with a differential box-counting approach to differentiate the texture characteristics of cloud and fog, and then the spectral and texture features are combined using an NDFI weighted fractal dimension algorithm as a new feature to improve the existing daytime fog detection approach. The performance of this approach is evaluated against ground-based measurements over China in winter, and the approach is proved to be effective in detecting land fog accurately based on the three cases.     

Classification of electromyography signals using relevance vector machines and fractal dimension

Neural Computing and Applications - Surface electromyography (EMG) signals have been studied extensively in the last years aiming at the automatic classification of hand gestures and movements as...     

Feature selection using correlation fractal dimension: Issues and applications in binary classification problems

Feature selection methods can be classified broadly into filter and wrapper approaches. Filter-based methods filter out features which are irrelevant to the target concept by ranking each feature according to some discrimination measure and then select features with high ranking value. In this paper, a filter-based feature selection method based on correlation fractal dimension (CFD) discrimination measure is proposed. One of the subgoals of this paper is to outline some issues that arise while calculating fractal dimension for higher dimensional ‘empirical’ data sets. It is well known that the calculation of fractal dimension for empirical data sets is meaningful only for an appropriate range of scales. We demonstrate through experimentation on data sets of various sizes that fractal dimension-based algorithms cannot be applied routinely to higher dimensional data sets as the calculation of fractal dimension is inherently sensitive to parameters like range of scales and the size of the data sets. Based on the empirical analysis, we propose a new feature selection technique using CFD that avoids the above issues. We successfully applied the proposed algorithm on a challenging classification problem in bioinformatics, namely, Promoter Recognition.     

基于小波与数学形态学的木材缺陷检测

木材缺陷检测是木材加工中的重要步骤,为了实现木材缺陷自动检测,提出了一种基于小波与数学形态学的缺陷检测方法。首先用多尺度小波对缺陷图像进行分解,滤除缺陷图像中的干扰信息,然后进行小波重构,在重构图像上进行形态学bottom-hat变换,结合阈值处理和区域生长检测出各种木材缺陷。实验表明,该方法具有高效准确的特点,能够满足木材加工过程缺陷检测的实际需求。     

Efficient surface defect detection using self-supervised learning strategy and segmentation network

Surface defect detection plays a crucial role in the production process to ensure product quality. With the development of Industry 4.0 and smart manufacturing, traditional manual defect detection becomes no longer satisfactory, and deep learning-based technologies are gradually applied to surface defect detection tasks. However, the application of deep learning-based defect detection methods in actual production lines is often constrained by insufficient data, expensive annotations, and limited computing resources. Detection methods are expected to require fewer annotations as well as smaller computational consumption. In this paper, we propose the Self-Supervised Efficient Defect Detector (SEDD), a high-efficiency defect defector based on self-supervised learning strategy and image segmentation. The self-supervised learning strategy with homographic enhancement is employed to ensure that defective samples with annotations are no longer needed in our pipeline, while competitive performance can still be achieved. Based on this strategy, a new surface defect simulation dataset generation method is proposed to solve the problem of insufficient training data. Also, a lightweight structure with the attention module is designed to reduce the computation cost without incurring accuracy. Furthermore, a multi-task auxiliary strategy is employed to reduce segmentation errors of edges. The proposed model has been evaluated with three typical datasets and achieves competitive performance compared with other tested methods, with 98.40% AUC and 74.84% AP on average. Experimental results show that our network has the smallest computational consumption and the highest running speed among the networks tested.     

Automated steel surface defect detection and classification using a new deep learning-based approach

Neural Computing and Applications - In this study, a new deep learning-based approach has been developed that detects and classifies surface defects that occur in the steel production process. The...     

An effective framework using identification and image reconstruction algorithm for train component defect detection

Applied Intelligence - Under long-term high-speed movement, the precision components of trains are extremely prone to defects, which could potentially endanger the safe operation of the train....     

Void defect detection in ball grid array X-ray images using a new blob filter

Ball grid arrays (BGAs) have been used in the production of electronic devices/assemblies because of their advantages of small size, high I/O port density, etc. However, BGA voids can degrade the performance of the board and cause failure. In this paper, a novel blob filter is proposed to automatically detect BGA voids presented in X-ray images. The proposed blob filter uses the local image gradient magnitude and thus is not influenced by image brightness, void position, or component interference. Different sized average box filters are employed to analyze the image in multi-scale, and as a result, the proposed blob filter is robust to void size. Experimental results show that the proposed method obtains void detection accuracy of up to 93.47% while maintaining a low false ratio. It outperforms another recent algorithm based on edge detection by 40.69% with respect to the average detection accuracy, and by 16.91% with respect to the average false ratio.     

Evaluating autostereoscopic 3D baseball games using signal detection theory and receiver operating characteristic space

This study investigates the effects of display technique (2D, autostereoscopic 3D), ball speed (138, 140 km/h), and operation time (5, 10, 15 min) on the four outcomes of signal detection theory (SDT) (i.e., hit, miss, false alarm, correct rejection), β, d’, receiver operating characteristic (ROC) space, Simulator Sickness Questionnaire (SSQ), and the iGroup Presence Questionnaire (IPQ). The results indicated that the display technique was significant on the hit rate, SSQ, and IPQ, where a higher hit rate, visual fatigue, and IPQ were found in the 3D technique. The results also showed that the ball speed was significant on the miss rate and d’, where a low miss rate and high d’ were found in the higher speed of 138 km/h. The results further demonstrated that the operation time was significant on the false alarm rate, correct rejection rate, d’, SSQ, and IPQ, with a long operation time being associated with a better performance for every variable, except the SSQ.Relevance to industryFrom the results obtained regarding the ROC space, this study found that the possibility of participants’ misjudgements was low, and the accuracy of this research is considered reliable. The results of this study could serve as a reference for N3DS and game manufacturers in designing future products.     

A novel defect detection and identification method in optical inspection

Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect detection and pattern recognition algorithms are in great demand to find out closely related causes. Modifying the manufacturing processes can eliminate defects, and thus to improve the yield. Defect patterns such as rings, semicircles, scratches, and clusters are the most common defects in the semiconductor industry. Conventional methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach is proposed in this paper to detect these defect patterns in noisy images. First, a novel scheme is developed to simulate datasets of these 4 patterns for classifiers’ training and testing. Second, for real optical images, a series of image processing operations have been applied in the detection stage of our method. In the identification stage, defects are resized and then identified by the trained support vector machine. Adaptive resonance theory network 1 is also implemented for comparisons. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.     

基于分形和肤色模型的自然态人脸检测方法研究

在对当前人脸检测问题研究过程中常用方法及其特点进行论述基础上,分析了利用分形特征实现人脸检测的可行性,提出了基于分形和肤色模型的自然态人脸检测方法.通过大量实验可知,该算法既克服了传统的基于启发式(知识)模型的方法和基于统计模型方法适应变化能力差、检测速度慢的缺点,又克服了单纯的肤色模型算法对肤色相近区域误检率高的缺点,利用人脸图像所特有分形特征,可以实现自然态环境下,多角度、多姿态人脸检测.     

视线追踪系统眼部特征检测及视线方向计算方法研究

基于单视觉主动红外光源系统,提出了一种视线检测方法．在眼部特征检测阶段,采用投影法定位人脸;根据人脸对称性和五官分布的先验知识,确定瞳孔潜在区域;最后进行人眼特征的精确分割．在视线方向建模阶段,首先在头部静止的情况下采用非线性多项式建立从平面视线参数到视线落点的映射模型;然后采用广义回归神经网络对不同头部位置造成的视线偏差进行补偿,使非线性映射函数扩展到任何头部位置．实验结果及在交互式图形界面系统中的应用验证了该方法的有效性．     

Signature analysis and defect detection in layered manufacturing of ceramic sensors and actuators

This paper presents the concept of a process signature for the use of online signature analysis and defect detection in the layered manufacturing (LM) of ceramic sensors and actuators. To achieve the high quality of parts built by the fused deposition of ceramics (FDC), an online process-monitoring system is implemented to detect the processing defects. Using a process signature extracted from the image of a layer captured by the monitoring system, an ideal image is created that is then compared to the original image to detect and identify the defects. Some results of signature analysis and defect detection for single-material and multi-material parts are also presented.Received: 22 July 1999, Accepted: 21 October 2001, Published online: 29 October 2003 Correspondence to: Mohsen A. JafariThis work was supported by the Office of Naval Research under grant # N-0014-96-1-1175. Ref. US Patent # S-5738817, April 14, 1998.     

Effect of bright and shade,and luminance difference of defect on defect detection in appearance inspection utilizing peripheral vision

This study considers the effect of bright and shade, and luminance difference of defects on defect detection in appearance inspection utilizing peripheral vision experimentally. Specifically, bright and shade of defect, luminance difference of defect, surface luminance of defect (evaluation index of the difficulty of defect detection, which was proposed in the previous study), and location of defect are designed as experimental factors, and their effects on defect detection rate are evaluated. As a result, it is clarified that the defect detection rate of the shade defects is lower than that of the bright defects, even if the surface luminance is at an identical level. It is also clarified that the defect detection rate of the luminance difference of 10 cd/m² becomes lower even if the surface luminance is at an identical level. Furthermore, these two trends are particularly remarkable for defects detected in the peripheral vision. Based on the results, in actual appearance inspection utilizing peripheral vision, it is necessary to take measures to detect the shade defects and/or the luminance difference of approximately 10 cd/m².     

Bi- and three-dimensional urban change detection using sentinel-1 SAR temporal series

GeoInformatica - Urban areas are subject to multiple and very different changes, in a two- and three-dimensional sense, mostly as a consequence of human activities, such as urbanization, but also...