In video surveillance, detection of moving objects from an image sequence is very important for target tracking, activity recognition, and behavior understanding. Background subtraction is a very popular approach for foreground segmentation in a still scene image. In order to compensate for illumination changes, a background model updating process is generally adopted, and leads to extra computation time. In this paper, we propose a fast background subtraction scheme using independent component analysis (ICA) and, particularly, aims at indoor surveillance for possible applications in home-care and health-care monitoring, where moving and motionless persons must be reliably detected. The proposed method is as computationally fast as the simple image difference method, and yet is highly tolerable to changes in room lighting. The proposed background subtraction scheme involves two stages, one for training and the other for detection. In the training stage, an ICA model that directly measures the statistical independency based on the estimations of joint and marginal probability density functions from relative frequency distributions is first proposed. The proposed ICA model can well separate two highly-correlated images. In the detection stage, the trained de-mixing vector is used to separate the foreground in a scene image with respect to the reference background image. Two sets of indoor examples that involve switching on/off room lights and opening/closing a door are demonstrated in the experiments. The performance of the proposed ICA model for background subtraction is also compared with that of the well-known FastICA algorithm.

Fault detection and classification (FDC) is important for semiconductor manufacturing to monitor equipment's condition and examine the potential cause of the fault. Each equipment in the semiconductor manufacturing process is often accompanied by a large amount of sensor readings, also called status variable identification (SVID). Identifying the key SVIDs accurately can make it easier for engineers to monitor the process and maintain the stability of the process and wafer productive yields. This article proposes using the random forests algorithm to analyze the importance of SVIDs of equipment sensors, automatically filters the key SVID by using k-means, and integrates various machine learning methods to verify the key SVIDs and identify key processing time and steps. Upon the key parameters are identified, the key processing time and steps are investigated subsequently. The ensemble models constructed on k-nearest neighbors (kNNs) and naïve Bayes classifiers are presented for classifying wafers as normal or abnormal. Data visualization of multidimensional key SVIDs is performed by using t-distributed stochastic neighbor embedding (t-SNE) to create a graphical aid in FDC for the process engineer. An empirical study is conducted to validate the proposed data-driven framework for fault detection and diagnostic. The experimental results demonstrate that the proposed framework can detect abnormality effectively with highly imbalanced classes and also gain insightful information about the key SVIDs and corresponding key processing time and steps.

For defect detection in non-periodical pattern images, such as printed-circuit boards (PCBs) or integrated circuit (IC) dies found in the electronic industry, template matching could be the only applicable method to tackle the problem. The traditional template matching techniques work in the spatial domain and rely on local pixel information. They are sensitive to geometric and lighting changes, and random product variations. The currently available Fourier-based methods mainly work for plain and periodical texture surfaces. In this paper, we propose a global Fourier image reconstruction method to detect and localize small defects in non-periodical pattern images. It is based on the comparison of the whole Fourier spectra between the template and the inspection image. It retains only the frequency components associated with the local spatial anomaly. The inverse Fourier transform is then applied to reconstruct the test image, where the local anomaly will be restored and the common pattern will be removed as a uniform surface. The proposed method is invariant to translation and illumination, and can detect subtle defects as small as 1-pixel wide in a wide variety of non-periodical patterns found in the electronic industry.

This article presents a deep learning scheme for automatic defect detection in material surfaces. The success of deep learning model training is generally determined by the number of representative training samples and the quality of the annotation. It is extremely tedious and tiresome to annotate defects pixel-by-pixel in an image to train a semantic network model for defect segmentation. In this study, we propose a two-stage deep learning scheme to tackle the pixel-wise defect detection in textured surfaces without manual annotation. The first stage of the deep learning scheme uses two cycle-consistent adversarial network (CycleGAN) models to automatically synthesize and annotate defect pixels in an image. The synthesized defect images and their corresponding annotated results from the CycleGAN models are then used as the input–output pairs for training the U-Net semantic network. The proposed scheme requires only a few real defect samples for the training and completely requires no manual annotation work. It is practical and computationally very efficient for the implementation in manufacturing. Experimental results show that the proposed deep learning scheme can be applied for defect detection in a variety of textured and patterned surfaces, and results in high detection accuracy.

Precision positioning is very important for automatic assembly and inspection in the electronic manufacturing process. In this paper, we propose a fast image alignment method using the expectation–maximization (E–M) technique. The proposed algorithm is especially applied to positioning and defect inspection of printed circuit boards (PCBs). It can well handle deformed or incomplete object shapes with translation, rotation, and scale changes. The Canny edge detector is used to generate the edge maps of images. The E-step of the E–M procedure finds mutual edge points in both compared images by assigning weights to individual edge points. The mutual edge points give larger weights, while the foreign edge points in two images have smaller weights. The M-step then calculates the geometric transformation parameters using the weighted edge points in individual images. For an edge point in one image, a fast spiral search is proposed to find its corresponding edge point with the shortest distance in the other image. The spiral search is carried out by a predetermined lookup table, and no computation is involved in the search process. The weight of each edge point is inversely proportional to the neighboring distance. Experimental results indicate that the proposed E–M positioning method can achieve a translation error less than 1 pixel and a rotation error smaller than 1° for PCB positioning.