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Despite the influx of companies and systems offering automatic inspection for the PCB industry, its acceptance rate in Europe has been slow. The problem seems to be lack of machine intelligence and inflexibility—factors that European companies cannot tolerate due to the nature of the market and the product mix. A new optical inspection system based on the ‘comparison’ method offers the required flexibility, but has needed a great deal of research into mechanics, electronics and software to overcome inherent problems. As this programme nears completion, a new generation of automatic optical inspection and statistical control systems will be possible.

Today, medical models can be made by the use of medical imaging systems through modern image processing methods and rapid prototyping (RP) technology. In ultrasound imaging systems, as images are not layered and are of lower quality as compared to those of computerized tomography (CT) and magnetic resonance imaging (MRI), the process for making physical models requires a series of intermediate processes and it is a challenge to fabricate a model using ultrasound images due to the inherent limitations of the ultrasound imaging process. The purpose of this paper is to make high quality, physical models from medical ultrasound images by combining modern image processing methods and RP technology.

A novel and effective semi‐automatic method was developed to improve the quality of 2D image segmentation process. In this new method, a partial histogram of 2D images was used and ideal boundaries were obtained. A 3D model was achieved using the exact boundaries and then the 3D model was converted into the stereolithography (STL) format, suitable for RP fabrication. As a case study, the foetus was chosen for this application since ultrasonic imaging is commonly used for foetus imaging so as not to harm the baby. Finally, the 3D Printing (3DP) and PolyJet processes, two types of RP technique, were used to fabricate the 3D physical models.

The physical models made in this way proved to have sufficient quality and shortened the process time considerably.

It is still a challenge to fabricate an exact physical model using ultrasound images. Current commercial histogram‐based segmentation method is time‐consuming and results in a less than optimum 3D model quality. In this research work, a novel and effective semi‐automatic method was developed to select the threshold optimum value easily.

To enhance the detection efficiency of printed circuit board (PCB) soldered dot, this study aims to detect PCB soldered dot with image processing method.

Through a series of image processing algorithms, threshold segmentation and feature extraction of the solder joint images were completed; then, sphericity was confirmed based on the area and perimeter, as well as the shape parameter and eccentricity ratio of the calculated region, paving the way for defect pattern recognition.

It proves that the method with high efficiency and precision can satisfy the requirements of PCB soldered dot detection.

This paper is original in presenting a method for PCB welding spot detection.

The exponential growth in computational capabilities and the increasing reliability of current simulation tools have fostered the use of computational fluid dynamics (CFD) in the design of pioneering aircraft engine architectures, such as the counter rotating open rotor (CROR) engine. Today, this design process is led by tight performance and noise constraints from a very early stage, thus requiring deep investigations of the aerodynamic and acoustic behaviour of the fluid flow. The purpose of this study is to track the trajectory of tip vortices, which is of critical importance to understand and prevent potential vortex–blade interactions with subsequent rows, as this condition strongly influences the aerodynamic and structural performance and acoustic footprints of the engine.

In this paper, a flow feature detection methodology is applied to a particular CROR test case with the goal of visualizing and tracking the development of these coherent structures from the tip of front rotating blades. The suitability and performance of four typical region-based methodologies and one line-based (LB) criteria are firstly evaluated. Then, two novel seeding methodologies are presented as an attempt to improve the performance of the LB algorithm previously investigated.

It was demonstrated that the new seeding algorithms increase the probability of the selected seeds to grow into a tip vortex line and reduce the user’s dependence upon the selection of candidate seeds, providing faster and more accurate answers during the design-to-noise iterative process.

Apart from the new vortex detection initialization methodologies, the paper also attempts to assist the user in the endeavour of extracting rotating structures from their own CFD simulations.

The purpose of this paper is to introduce an innovative procedure for digital historical documents image binarization based on image pre‐processing and image condition classification. The estimated results for each class of images and each method have shown improved image quality for the six categories of document images described by their separate characteristics.

The applied technique consists of five stages, i.e. text image acquisition, image preparation, denoising, image type classification in six categories according to image condition, image thresholding and final refinement, a very effective approach to binarize document images. The results achieved by the authors' method require minimal pre‐processing steps for best quality of the image and increased text readability. This methodology performs better compared to current state‐of‐the‐art adaptive thresholding techniques.

An innovative procedure for digital historical documents image binarization based on image pre‐processing, image type classification in categories according to image condition and further enhancement. This methodology is robust and simple, with minimal pre‐processing steps for best quality of the image, increased text readability and it performs better compared to available thresholding techniques.

The technique consists of limited but optimized pre‐processing sequential steps, and attention should be given in document image preparation and denoising, and on image condition classification for thresholding and refinement, since bad results in a single stage corrupt the final document image quality and text readability.

The paper contributes in digital image binarization of text images suggesting a procedure based on image preparation, image type classification and thresholding and image refinement with applicability on Byzantine historical documents.

The purpose of this paper is to provide crystal size distribution (CSD) using photogrammetric and image analysis techniques. A new algorithm is proposed to detect CSDs and a comparison is carried out with conventional watershed segmentation algorithm.

Polished granite plates were prepared to designate the metrics of CSD measurements. There are many important metrics for measurements on CSD. Some of them are orientation, size, position, area, aspect ratio, convexity, circularity, perimeter, convex hull, bounding box, eccentricity, shape, max-min length of CSD's fitted and corrected ellipse, and population density in a per unit area. Prior to image processing stage, camera calibration was performed to remove the image distortion errors. Image processing techniques were applied to corrected images for detecting the CSD parameters.

The proposed algorithm showed the improved preservation of size and shape characteristics of the crystal material when compared to the watershed segmentation. According to the experimental results, proposed algorithm revealed promising results in identifying CSDs more easily and efficiently.

This paper describes CSD of granitic rocks by using automated grain boundary detection methods in polished plate images. Some metrics of CSDs were detected by employing a new procedure. A computer-based image analysis technique was developed to measure the CSDs on the granitic rock plates. A validation is done by superimposing digitally detected CSD metrics to original samples.

The relationships among multi‐dimensional data (such as medical examination data) with ambibuity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back‐propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi‐dimensional feature selection. The first one proposed is a feature selection procedure from the trained back‐propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis‐Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi’s fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD; it can deal with a reduced model, which is the focus of this paper. In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

The purpose of this paper is to propose a defect detection method of silicone caps positional deviation on flexible printed circuit board (FPCB) of keyboard based on automatic optical inspection.

First, the center of silicone caps of target keyboard FPCB image was extracted as feature points for generating the feature image which is used for registration rigidly with the reference feature image generated from the CAD drawings. Then, a flexible image registration method based on the surrounding-control-center B-splines (SCCB) strategy was proposed, which could correct the flexible deformation of the image generated by FPCB substrate while keeping the pasting deviation information about silicone caps unchanged. Finally, on this basis, a nearest neighbor strategy was proposed to detect the positional deviation of silicone caps.

Experimental results show that the proposed method can effectively detect the positional deviation defect of silicone caps. The G-mean value of the proposed method is 0.941746, which is 0.3 higher compared to that of similar research.

This paper presents a method to detect positional deviation defect of silicone caps on keyboard FPCB. Different from the classic B-spline image registration method, the proposed SCCB method used the neighborhood information of the pixel to be registered selectively to calculate the displacement vector needed for its registration, which overcame the problem that the silicone cap pasting deviation information disappears with the correction of the flexible deformation of the image.

The purpose of this paper is to develop a robot vision system for surface defect detection of 3D objects. It aims at the ill‐defined qualitative items such as stains and scratches.

A robot vision system for surface defect detection may counter: high surface reflection at some viewing angles; and no reference markers in any sensed images for matching. A filtering process is used to separate the illumination and reflection components of an image. An automatic marker‐selection process and a template‐matching method are then proposed for image registration and anomaly detection in reflection‐free images.

Tests were performed on a variety of hand‐held electronic devices such as cellular phones. Experimental results show that the proposed system can reliably avoid reflection surfaces and effectively identify small local defects on the surfaces in different viewing angles.

The results have practical implications for industrial objects with arbitrary surfaces.

Traditional visual inspection systems mainly work for two‐dimensional planar surfaces such as printed circuit boards and wafers. The proposed system can find the viewing angles with minimum surface reflection and detect small local defects under image misalignment for three‐dimensional objects.

In a previous volume of this journal Miss Shazeeda Ali made a brief analysis of the Jamaican Money Laundering Act (MLA) in which she made a number of important observations. Since that time, the MLA has been amended twice, the last time in March 1999. The purpose of this article is to highlight the main features of the amendments.