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TALENT3 (Research in automated inspection for quality control in the manufacturing industry) is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). The general objective of this project is to advance in the design, implementation and industrialization of contact-free and occlusions-free automated inspection systems, introducing Machine Learning techniques. This will enable companies of the Valencia Region to increase product quality guarantees and thus strengthen their market position. This document corresponds to a poster of the project as one of the dissemination activities., TALENT. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of the Valencian Community for the development of non-economic R&D projects carried out in cooperation with companies during 2019 with 229.592,30€. File number: IMDEEA/2019/86.

The radiation field adjustable collimator is a mechatronics product. The rectangle opening of collimator is mainly composed of four collimation blocks. Every collimation block has 2 degrees of freedom (DOF). And these collimation blocks are arranged stagger from end to end. The X-ray irradiation field is automatically adjusted by vertical and horizontal motors. The irradiation field adjustable collimator can meet the need of X-ray sources for different fields in industrial inspection and industrial CT. The collimation blocks are arranged in the same plane, which has a compact structure and effectively reduces the leakage of stray X-rays.

In typical Intelligence Surveillance and Reconnaissance (ISR) missions, persistent surveillance is commonly defined as the exercise of automatic intelligence discovery by monitoring a wide area coverage at a high altitude leveraging an aerial platform (manned or unmanned). It can be large enough to carry a matrix of high resolution cameras and a rack of high performance computing images processing and exploitation units (PEU). The majority of the small unmanned aerial vehicles (sUAV) are able to carry optics payloads, allowing them to take aerial images from strategic viewpoints. This capability constitutes a key enabler for an immense number of applications, such as crowd monitoring, search and rescue, surveillance scenario, industrial inspection and so on. The constrained onboard processing power in addition to the strict limit in the flying time of sUAV are amongst the serious challenges that have to be overcome to enable a cost effective persistent surveillance based on sUAV platforms. In this paper, we conduct a feasibility study for developing a potential sUAV based persistent surveillance system with tethered power supply.

Industrial facilities often require periodic visual inspections of key installations. Examining these points of interest is time consuming, potentially hazardous or require special equipment to reach. MAVs are ideal platforms to automate this expensive and tedious task. In this work we present a novel system that enables a human operator to teach a visual inspection task to an autonomous aerial vehicle by simply demonstrating the task using a handheld device. To enable robust operation in confined, GPS-denied environments, the system employs the Google Tango visual-inertial mapping framework as the only source of pose estimates. In a first step the operator records the desired inspection path and defines the inspection points. The mapping framework then computes a feature-based localization map, which is shared with the robot. After take-off, the robot estimates its pose based on this map and plans a smooth trajectory through the way points defined by the operator. Furthermore, the system is able to track the poses of other robots or the operator, localized in the same map, and follow them in real-time while keeping a safe distance., Accepted - Workshop on Aerial Robotic Inspection and Maintenance

A novel inspection sensor by using an edge feature description (EFD) algorithm based on a support vector machine (SVM) is proposed for industrial inspection of images. This method detects and adaptively segments blurred images by using the proposed algorithm, which uses EFD to effectively classify blurred samples and improve the conventional methods of inspecting blurred objects; the algorithm selects and optimally tunes suitable features. The proposed sensor applies a suitable feature-extraction strategy on the basis of the sensing results. Experimental results demonstrate that the proposed method outperforms the existing methods.

Fast and reliable industrial inspection is a main challenge in manufacturing scenarios. However, the defect detection performance is heavily dependent on manually defined features for defect representation. In this contribution, we investigate a new paradigm from machine learning, namely deep machine learning by examining design configurations of deep Convolutional Neural Networks (CNN) and the impact of different hyper-parameter settings towards the accuracy of defect detection results. In contrast to manually designed image processing solutions, deep CNN automatically generate powerful features by hierarchical learning strategies from massive amounts of training data with a minimum of human interaction or expert process knowledge. An application of the proposed method demonstrates excellent defect detection results with low false alarm rates.

The quality of 3D point cloud captured by a structured light 3D camera, such as the density and accuracy of the points, depends upon the optimal settings of 3D camera exposure time. The fixed exposure time often set manually, may not be effective under the variation of ambient conditions, e.g., surface reflectance and illumination that incur saturation and/or under-exposure. This is especially the case where 3D cameras are to apply to industrial inspection. In this paper, a novel method for automatically determining the optimal exposure time of a 3D camera based on estimating the pixel intensity-shutter time slope is proposed. The experiments verify that a dense yet accurate 3D point cloud can be captured under varying ambient conditions: up to 40% improvement over the manual setting for the density of captured point cloud.

In this paper we introduce Machine Vision System (MVS) for industrial quality control inspections presenting new perspectives with the recent developments of Artificial Intelligence (AI). A brief literature review is provided which indicates a substantial growth of machine vision new studies and an improved workflow is proposed to include these findings. Besides already existing machine vision solutions there is space to increase detection in quality control inspection and reduce current implementation constraints and technical limitations. The paper shows MVS new development and evinces that a deeper understanding of AI, MVS limitations is needed to provide a clearer path for future studies.

This work addresses the problem of semi-automatic inspection and navigation in confined environments. A system that overcomes many challenges at the state of the art is presented. It comprises a multirotor able to inspect an industrial combustion chamber thus working in a GPS-denied environment with poor lighting conditions, in the presence of magnetic and communication disturbances, iron dust and repetitive patterns on the structure walls. The presented system is able to pass through narrow entrances but still capable of acquiring high resolution images and to allow operators to perform inspection of the structures. Starting from the captured data, the system is able to provide a 3D reconstruction of the inspected environment for offline analysis.

TALENT2 (Research in Machine Learning techniques applied for open issues within the Industrial Manufacturing sector) is a project funded by the Valencian Institute for Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (FEDER). The general objective of this project is to advance in the design, implementation and industrialization of contact-free and occlusions-free automated inspection systems, introducing Machine Learning techniques. This will enable companies of the Valencia Region to increase product quality guarantees and thus strengthen their market position. This deliverable is part of the PT3 work package "Dissemination", which aims to disseminate the scientific and technical results of the project, once the dissemination strategy has been defined at the beginning of the project; the results of the different dissemination activities carried out continuously have been evaluated. out and adapting the plan according to the results of it., TALENT II. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of the Valencian Community for the development of non-economic R&D projects carried out in cooperation with companies during 2018 with 200.000€. File number: IMDEEA/2018/115

The use of autonomous drones in industrial inspection is gaining momentum with improvements in hardware and control. Considering the availability of historical data as drones gather information by regular sorties, a new opportunity for change detection is emerging for inspection and maintenance. In this paper, we propose a visual change detection framework using multi- scale super pixel approach. A framework that can (i) automatically capture appropriate images that match the stored images in the data agnostic drone store, and (ii) perform visual change detection is proposed. The algorithm is tested on a street-view change detection dataset containing images from 152 categories. Image matching step resulted in an accuracy of over 90%. On the street-view dataset, a change detection rate of over 90% is achieved with 60% categories detecting more than 60% of changed region. The proposed approach achieves better performance compared to other state-of-the- art methods that use image descriptors. The capability is implemented on an off-the-shelf drone to demonstrate the utility.

Camera calibration is a widely covered and wellknown topic, which has been discussed and well described in many publications. However, the detection of the corresponding calibration markers can be problematic if the imaging setup considers a very short working distance, a huge magnification and special lighting setups, which is the case in almost every industrial inspection system, especially in surface inspection. Low-cost calibration targets can have poor quality in terms of surface homogeneity, resulting in reflection and illumination artifacts. In this paper, we present a distance transform based approach for detecting calibration markers of those targets. As an example application, the typical setup of a surface inspection system for printed circuit boards is used. This approach ensures a built-in calibration process without the need of changing the imaging setup.

Infrared thermal imaging is a very rapidly evolving field. The latest trends are small smartphone IR camera accessories, making infrared imaging a widespread and well-known consumer product. Applications range from medical diagnosis methods via building inspections and industrial predictive maintenance etc. also to visualization in the natural sciences. Infrared cameras do allow qualitative imaging and visualization but also quantitative measurements of the surface temperatures of objects. On the one hand, they are a particularly suitable tool to teach optics and radiation physics and many selected topics in different fields of physics, on the other hand there is an increasing need of engineers and physicists who understand these complex state of the art photonics systems. Therefore students must also learn and understand the physics underlying these systems.

Sparse-view Computed Tomography (CT) plays an important role in industrial inspection and medical diagnosis. However, the established reconstruction equations based on traditional Radon transform are ill-posed and obtain an approximate solution in the case of finite sampling angles. By contrast, Mojette transform is considered as the discrete geometry of the projection and reconstruction lattice. It determines the geometrical conditions for ensuring a unique solution instead of solving an ill-posed problem from the start. Therefore, Mojette transform results in theoretical exact image reconstruction in the discrete domain, and approximately gets the minimum number of projections, as well as their directions. However, the reconstruction method utilizing Mojette transform is very sensitive to noise. To address the problem, the paper proposes a sparse-view Mojette inversion algorithm based on the minimum noise accumulation by selecting the prioritized projections for an image reconstruction. Experimental results show that the proposed method can effectively suppress the noise accumulation without increasing the number of projections and produce better reconstruction results than traditional corner-based Mojette inversion (CBI).

A large number of real-world machine learning and data mining problems have several crucial issues that need to be solved, such as class imbalance and multiple objectives; these issues cannot be easily overcome with many learning methods. This study proposes an efficient approach to address the issues and applies the approach in the design of a machine vision system for a real-world problem: inline inspection of surface defects on glass substrates of thin-film transistor liquid crystal displays (TFT-LCDs). The three major steps in the machine vision system are: (1) selective extraction of defect features from images using 2-dimemnsional wavelet decomposition, (2) training cost-sensitive classifiers to handle class imbalance, and (3) use of ensemble techniques to achieve multiple manufacturing objectives. When applied to an industrial case study, the achieved performance shows that using the proposed approach in defect inspection of TFT-LCD glass substrates is a viable alternative to manual inspection.

Three-dimensional (3D) imaging technique based on structured illumination has been deeply studied and widely applied in recent years. Among numerous 3D imaging methods, the imaging technique based on Gray-coded structured illumination has been applied in industrial inspection, digitalization of antique, biomedicine detection and so on, owing to its robustness and anti-noise ability. To realize 3D imaging, the projected Gray-coded pattern can either be used to modulate the height information or be used to assist acquisition and computation of the height information of the tested object. In this paper, a review was given to introduce principles and progresses of the imaging techniques based on Gray-coded structured illumination and the research achievements of the author's research group about this technique were also shown. In addition, the advantages, disadvantages and application scope of binary Gray-coded patterns in 3D shape measurement were discussed and the developing trends of this technique was pointed out as well.

SUMMARY This paper presents a novel low cost learning algorithm for a test feature classifier by use of an overlap index list (OIL). In general, classifiers need a large amount of training data to achieve the high performance, which results in long computation times. The proposed algorithm using OIL can maintain search and check elemental combinatorial features from lower dimensions up to higher ones. Classification problems in real industrial inspection lines have been solved by the proposed algorithm, and large reductions in computation time have been obtained.

This paper provides an overview of state-of-the-art 3-D endoscopic imaging technologies. Physical objects in the world are 3-D, yet traditional endoscopes can only acquire 2-D images that lack depth information. This fundamental restriction greatly limits our ability to perceive and understand the complexity of real world objects. Lack of 3-D information also hinders our ability to quantitatively measure 3-D objects. In both medical imaging and industrial inspection applications, 3-D surface imaging capability would add one more dimension, literally and figuratively, to the existing imaging technologies. Over the past decades, tremendous new technologies and methods emerged in the 3-D surface imaging field. In this paper, we first provide a classification of these technologies. We then describe each category in detail, with representative designs and examples. This overview would be useful to researchers in the field since it provides a snapshot of the current state-of-the-art, from which subsequent research in meaningful directions is encouraged. This overview also contributes to the efficiency of research by preventing unnecessary duplication of already performed research.

The weld defects are formed due to the incorrect welding patterns or wrong welding process. The defects in the weld may vary from size, shape and their projected quality. The most common weld defects occur during welding process is slag inclusions, porosity, lack of fusion and incomplete penetration. In this study, an effective method for weld defect classification using machine learning algorithm is presented. The system uses Speeded-up Robust Features (SURF) for feature extraction and one of the machine learning algorithms called Auto-Encoder Classifier (AEC) for classification. Initially, the features that distinguish weld defects and no defects in the weld image are extracted by SURF. Then, AEC is analyzed for weld image classification using different number of neurons in different hidden layers (2 and 3 hidden layers). The performance of the system is evaluated by GD X-ray weld image database. The results show that the weld images are correctly classified with 98% accuracy using SURF and AEC.

Purpose – To avoid harm to humans, environment, and capital goods, hazardous or explosive gases that are possibly escaping from industrial and infrastructure facilities of the gas and oil processing industry have to be detected and located quickly and reliably. Project RoboGasInspector aims at the development and evaluation of a human-robot system that applies autonomous robots equipped with remote gas detection devices to detect and locate gas leaks. This article aims to focus on the usability of telemanipulation in this context. Design/methodology/approach – This paper presents four user studies concerning human-robot interfaces for teleoperation in industrial inspection tasks. Their purpose is to resolve contradictory scientific findings regarding aspects of teleoperation and to verify functionality, usability, and technology acceptance of the designed solution in the actual context of use. Therefore, aspects concerning teleoperation that were separately examined before are evaluated in an integrated way. Considered aspects are influence of media technology on telepresence, simulator sickness and head slaved camera control, usability of different input devices for telemanipulation, and identification of intuitive gestures for teleoperation of mobile robots. Findings – In general, the implemented interaction concepts perform better compared to conventional ones used in contemporary, actually applied robot systems. Otherwise, reasons are analyzed and approaches for further improvements are discussed. Exemplary results are given for each study. Originality/value – The solution combines several technical approaches that are so far separately examined. Each approach is transferred to the innovative domain of industrial inspections and its applicability in this context is verified. New findings give design recommendations for remote workplaces of robot operators.

This paper presents an alternative method of material damage evaluation based on the X-ray computer tomography-detected microdefects and multiscale computer simulation. This is achieved by developing a method of the digital diagnosis and full-field numerical calculation of material degradation in macroscopic material test specimens. The method comprised three basic components: (a) digital detection and processing of micro/mesoscale material defects of macroscopic material test specimens; (b) multilevel meshing and multilevel finite element analysis for evaluating local/global material degradation; and (c) synchronized experimental and numerical determination of material damage. The unique contributions of the proposed approach include (a) a multilevel finite element meshing and analysis scheme that makes the full-field estimation of material degradation in macroscopic test specimens computationally tractable on regular workstations, (b) full-field exploration of mesoscale material defects (i.e., those with a feature size from several micrometers to a few millimeters), which play a crucial role in failure analysis of engineering components, and (c) the proposed method offers a significantly better accuracy in estimating material degradation in terms of effective modulus than the conventional analytical models in continuum damage mechanics and micromechanics. Test results of aluminum alloys confirm the efficacy of our approach in the digital interrogation of material degradation.

In this work a new approach/method for detection of hidden defects by lateral thermal flows is proposed. The method is based on propagation of the thermal wave in a lateral direction in the sample volume and observation of the surface thermal distribution. This approach allows to reveal the hidden defects/cracks which are not detectable by conventional thermal inspection methods. The experimental investigations were conducted on steel samples with the defects which do not satisfy the detection criteria empirically established for thermal methods. The defects were successfully revealed. The proposed technique has a high sensitivity to hidden structures and can be applied in industrial inspection procedures.

We demonstrated GPU accelerated real-time confocal fluorescence lifetime imaging microscopy (FLIM) based on the analog mean-delay (AMD) method. The frame rate of our system was demonstrated to be 13 fps for a 200 × 200 pixel image when observing maize vascular tissue. This system can be utilized for observing dynamic biological reactions, medical diagnosis, and real-time industrial inspection.

SUMMARY This paper presents a novel low cost learning algorithm for a test feature classifier by use of an overlap index list (OIL). In general, classifiers need a large amount of training data to achieve the high performance, which results in long computation times. The proposed algorithm using OIL can maintain search and check elemental combinatorial features from lower dimensions up to higher ones. Classification problems in real industrial inspection lines have been solved by the proposed algorithm, and large reductions in computation time have been obtained.

Instituto Tecnológico de Informática has a non-economic Activities Plan (PROMECE) whose general objective is to strengthen the research lines in which the Institute works, within the scope of Information and Communication Technologies (ICT). Through this activities plan a work is carried out to transfer the results obtained in the execution of R+D+I projects within these lines or areas of action. The transfer actions are aimed at companies and the industrial sector and society as a whole. This document compiles the market studies carried out within the framework of the project which contemplate the study of the demand for services of advanced data analytics; the study of competitors in advanced data analytics services; and the market study of manufacturers of springs and pieces of wire as potential users of 3D industrial inspection technologies, PROMECE 2016. Project funded by the Valencian Institute of Business Competitiveness (IVACE) and the European Union through the European Regional Development Fund (ERDF), within the public grant program adressed to Technological Institutes of IVACE network. File number: IMAMCN/2016/1

The acquisition of three-dimensional surface data plays an increasingly important role in the industrial sector. Numerous 3D shape measurement techniques have been developed. However, there are still limitations and challenges in fast measurement of large-scale objects or high-speed moving objects. The innovative line scan technology opens up new potentialities owing to the ultra-high resolution and line rate. To this end, a sensor for in-motion continuous 3D shape measurement based on dual line-scan cameras is presented. In this paper, the principle and structure of the sensor are investigated. The image matching strategy is addressed and the matching error is analyzed. The sensor has been verified by experiments and high-quality results are obtained.

We address the comparison of contrast improvement obtained with a fully adaptive polarimetric imager and the best channel of a static polarimetric imager in the presence of both intensity and polarization differences between the target and the background. We develop an in-depth quantitative study of the performance loss incurred by a static imager compared to a fully adaptive one in this case. These results are useful to make a well-informed choice between these two polarimetric imaging architectures in a given application.

The interest in gonioapparent pigments (metallic, pearlescent, interference, or diffractive) has increased in the last few years, especially for applications in the automotive industry. To assure a proper characterization of colors with gonioapparent pigments, commercial devices have appeared to characterize the color in different geometries, which are called multiangle spectrophotometers. As the gonioapparent pigments and multiangle instruments are relatively new, no studies exist regarding the instrumental-based procedure followed in the industry, and if the results provided are in agreement with the observer perception. Consequently, the main objective of this study was to examine the correlation of the instrumental color differences with visual assessments. The instrumental color difference was calculated with the color difference formula AUDI2000 (specific for this sector) between the pairs of similar samples of three types of coated panels (solid, metallic, and pearlescent). The values measured by a telespectroradiometer in a directional lighting booth and the colorimetric values obtained by means of a multiangle spectrophotometer BYK-mac were considered for this purpose. Additionally, a visual experiment was conducted to quantify the color difference by using the gray-scale method. The results revealed that an acceptable instrumental correlation existed despite the visual and the instrumental correlation being worse. In particular, it was checked that observers accepted a larger number of color pairs, that is, the visual color difference was smaller than the tolerance demanded by the industry (derived from AUDI2000). The authors thank the Ministry of Economy and Competitiveness for the coordinated project “New developments in visual optics, vision and color technology” (DPI2011-30090-C02). Francisco Javier Burgos also thanks the Autonomous Government of Catalonia for his predoctoral fellowship grant and Omar Gómez to the Ministry of Economy and Competitiveness for his predoctoral fellowship (FPI BES-2012-053080).

The inspection of moving specular freeform surfaces is an industrial challenge so far largely unsolved, even for the qualitative case, i.e. the mere determination of the presence of surface defects as opposed to the quantitative reconstruction of a surface. Products produced in high quantities therefore still have to be inspected manually which is labour intensive, expensive, monotonous and subjective. We propose a novel hardware setup and methodology to overcome this shortfall. The reflection of a line laser from a moving surface is captured on a translucent screen; surface defects show as gaps or bulges. Two methods to extract the resulting information are proposed and ways for its interpretation are shown. The proposed method is very cost effective and easy to implement. While limitations to surface geometry exist and absolute precision is not achievable, it is shown that the system is able to reliably detect, characterise and localise a range of typical surface defects on moving glazed ceramic tiles, our example application. The method is however applicable to a wide range of hybrid and specular surfaces.

In this paper, the authors first summarize recent advances in digital metrology at the Virtual Engineering Laboratory, the University of Michigan - Dearborn. Different algorithms of surface denoising of laser scanning and computed tomography are then briefly discussed. Last, some challenges of digital metrology are provided for supporting high-precision digital manufacturing and industrial inspection.

Vibrothermography, or sonic IR, is a nondestructive evaluation technique used to find surface and near surface defects—such as cracks and delaminations—through observations of vibration-induced heat generation. This method has significant interest as an industrial inspection method, however, a lack of understanding of the fundamental physics governing the heat generation process has limited its application despite extensive theoretical, numerical simulation, and experimental work. Significant theoretical and numerical simulation work has been performed, but has yet to be rigorously verified experimentally. This paper presents experimental verification of the sources of heat generation in vibrothermography; specifically friction, plasticity, and viscoelasticity. Specific experimental evidence is presented that verifies each of these heat-generating mechanisms.

In some industrial inspection processes, multiprocess-handling workers are required to enter the results of visual inspection tests without using their hands because of efficiency or hygiene reasons. We have developed a hands-free visual inspection system by using saccadic latency, a temporal characteristic of saccadic eye movements. The proposed system is free from the Midas touch problem (i.e., the difficulty in developing an eye-typing interface owing to the difficulty in differentiating between intentional blinks and gazes and natural ones). For verifying the system, an experiment was conducted in which ten subjects performed a visual inspection task. The average defect detection rates were 99.4%, and no Midas touch–related errors were observed. Results of the error analysis showed that redesigning the system interface would lead to a further enhancement of the system performance. © 2011 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Automatic reading of pointer meters is of great significance to meter verification and industrial inspection robots. Many automatic reading algorithms of pointer meters based on machine vision have been proposed. However, the pointer shadow problem has not been effectively solved, which hinders the automatic reading of inspection robots in complex lighting environments. Also, pointer line recognition is easily influenced by the images around the pointer. With the aim of addressing these problems, this paper proposes a robust and high-precision pointer meter automatic reading algorithm. In meter center recognition using a round-degree method, this paper uses the eight chain code shape to represent the pixel image shape to prevent abnormal calculation of the round-degree. A novel binarization threshold determination method based on the symmetry degree is proposed to segment the pointer region, which effectively solves the pointer shadow problem. In addition, for pointer recognition, this paper proposes an improved random sample consensus (RANSAC) algorithm to overcome background image interference. The experimental results prove that the algorithm effectively removes pointer shadows and that the reading precision is high.

When an operator first detects unusual and/or infrequent or irregular signals in a system, the operator does not need to take any action, but must increase his/her level of attention and be alert for any more serious signals that may confirm a problem with the system. This is referred to as extended vigilance. The purpose of this study was to construct a fuzzy vigilance-measuring model for countering the loss of extended vigilance. The model extends two-valued logic (“Yes” or “No”) to multi-valued logic through fuzzy sets. Then a fuzzy logic alarm was developed by the model for combating the extended vigilance decrement. The first experiment compared the effect of the fuzzy measuring model with signal detection theory (SDT) and discussed the relationship between preliminary and extended vigilance for a simulated feed-water monitoring system. The results indicated that the sensitivity of the fuzzy vigilance-measuring model is better than index d ′ and β , and that the preliminary vigilance significantly influences the extended vigilance. The second experiment verified the effect of the fuzzy logic alarm. The results indicated that the effect of the fuzzy logic alarm for combating the extended vigilance decrement is significant. When the preliminary vigilance is poor, an appropriate alarm will improve the extended vigilance. However, if the preliminary vigilance is very poor, the improvement of the extended vigilance will be limited. Relevance to industry: The extended vigilance is a new and important issue in human performance problems in industry, particularly in such areas as air-traffic control, industrial inspection and power plant monitor. The measuring model of vigilance could be applied to develop an alarm for promoting supervisory performance of human and human–machine detectors.

On-line non-contact surface inspection with high precision is still an open problem. Laser triangulation techniques are the most common solution for this kind of systems, but there exist fundamental limitations to their applicability when high precisions, long standoffs or large apertures are needed, and when there are difficult operating conditions. Other methods are, in general, not applicable in hostile environments or inadequate for on-line measurement. In this paper we review the latest research in Conoscopic Holography, an interferometric technique that has been applied successfully in this kind of applications, ranging from submicrometric roughness measurements, to long standoff sensors for surface defect detection in steel at high temperatures.

In non-destructive testing, Computed Tomography (CT) is increasingly used for dimensional measurement tasks such as the industrial inspection of work pieces. Besides the determination of the measurement uncertainty, the improvement of the image quality is subject of current research in order to establish CT as a measuring device.

Advances in electronic technology have made integrated circuits (ICs) the fundamental components in all electronic devices. To increase their production yield by catching defects as early as possible, we need to perform quality assurance on the semiconductor dies during the assembly and packaging processes. A common approach is to employ machine vision to compare a test die with a "known good die". However, difficulties in ensuring identical imaging conditions (such as illumination) are limitations to this die-to-die comparison approach. Instead, in this work we develop a novel reference-free defect detection algorithm for an IC die by analyzing its image. By identifying intrinsic and extrinsic features of various segments in the image, we implement a classification scheme to identify whether the die is defective or not. We rely on the fact that normal ICs contain regular patterns, and the abnormal and irregular regions are classified as potential areas of defects. Experimental results show that the proposed reference-free defect detection algorithm can detect most of the defects from different types of IC dies, and can also correctly classify normal IC dies as non-defective. These results demonstrate the feasibility of the reference-free defect detection approach.

PurposeThe purpose of this paper is to present improvements in X‐ray equipment, which are leading to wider use.Design/methodology/approachDevelopments in X‐ray sources and detectors are described. This is followed by a review of the more innovative equipment available for security and industrial applications.FindingsTechnological developments have produced smaller, lighter X‐ray systems and extended their applications to on‐site work. Multiple wavelength systems distinguish between different materials, and stereo systems remove ambiguities from X‐ray security imaging and allow 3D gauging of industrial components.Originality/valueThe paper highlights the availability of portable X‐ray systems and explains the underlying technology.

This paper studies the application of advanced computer image processing techniques for solving the problem of automated defect detection for textile fabrics. A new defect detection scheme is proposed, which consists of an odd symmetric real-valued Gabor filter, an even symmetric real-valued Gabor filter and one smoothing filter. In developing the scheme, the Gabor filters are designed on the basis of the texture features extracted optimally from a non-defective fabric image by using a Gabor wavelet network (GWN). The performance of the proposed defect detection scheme is evaluated off-line by using a set of fabric images taken from a database consisting of a wide variety of homogeneous fabric images. The results exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed scheme. To evaluate the performance of the proposed defect detection scheme further, real-time tests are conducted by using a prototyped automated defect detection system. The experimental results obtained further confirm the efficiency, effectiveness and robustness of the proposed detection scheme.

Expiration date labels are ubiquitous in the food industry. With the passage of time, almost any food becomes unhealthy, even when well preserved. The expiration date is estimated based on the type and manufacture/packaging time of that particular food unit. This date is then printed on the container so it is available to the end user at the time of consumption. MONICOD (MONItoring of CODes); an industrial validator of expiration codes; allows the expiration code printed on a drink can to be read. This verification occurs immediately after printing. MONICOD faces difficulties due to the high printing rate (35 cans per second) and problematic lighting caused by the metallic surface on which the code is printed. This article describes a solution that allows MONICOD to extract shapes and presents quantitative results for the speed and quality.

Industrial inspection of parts produced has the objective to detect and locate defects during manufacturing, assess quality and further predict damage/failure during service. Aircraft manufacturers use novel structural materials, including composites and cellular structures, which are characterized by high strength-to-weight ratio. Development of new composite materials requires innovative inspection systems to comply with customer demands for a reliable and fast inspection of complex parts. Typical failure in an aircraft part occurs due to either manufacturing failure or fatigue failure. Nondestructive testing (NDT) is a powerful tool used in aerospace and allied industries for detecting and analyzing structural failure and damage to parts. Most commonly used NDT methods in aerospace industries are eddy current testing (ECT), fluorescent penetration inspection, infrared and thermography, ultrasonic testing, acoustic emission, shearography, and radiographic tests (X-ray computed tomography CT). These techniques are described in brief with some applications in aerospace industries.

Most real surfaces and objects show variations in appearance with viewing and illumination directions. Besides angular dependency, they also show spatial variation in color, i.e., they exhibit some sort of texture. Of the surfaces we see, surfaces colored by special-effect pigments produce several complex visual effects, like change in color and lightness with viewing and illumination angles, and effects like sparkle and gloss on other textures. In the last two decades, different commercial devices have appeared to help ensure the proper characterization of materials with special-effect pigments. However, the instrumental characterization of sparkle is currently available only by a commercial device integrated into a multi-angle spectrophotometer. As it is difficult to find complete open original studies about the sparkle effect for designing and calibrating this commercial instrument, the main objective of this work was to check whether a good visual and instrumental correlation exists between the sparkle that the observer perceives and the sparkle value provided by the device using some subsets of goniochromatic samples with different types of special-effect pigments and colors. Visual assessments were made by a conventional magnitude estimation method in a directional lighting booth, which belonged to the same company owner of the sparkle instrument, in different geometries and at distinct illuminance levels. The results revealed that there was a good visual correlation of the sparkle grade value. By separately analyzing the factors used in its instrument algorithm, such as sparkle intensity and sparkle area values, it was clearly shown that the correlation was not good or simply did not exist. Consequently, and perhaps in regards to the choice of new special-effect pigments, such as synthetic mica and other future ones, we generated herein even more questions about current mathematical algorithms, and only recognized calculating this texture effect at the industrial level. The authors are grateful to the EMRP for funding the project “Multidimensional Reflectometry for Industry”. The EMRP is jointly funded by EMRP participating countries within EURAMET and the European Union. We thank the Spanish Ministry of Economy and Competitiveness for the coordinated project DPI2011-30090-C02 and project DPI-2015-68514 and University of Alicante for project GRE13-28. Omar Gómez Lozano would also like to thank the above-cited ministry for his pre-doctoral fellowship (FPI BES-2012-053080).

The ImPACT program organized by the cabinet office of Japan started to develop 3D real-time photoacoustic imaging technology which is applicable for wider clinical filed based on the philosophy of “Vascular Health Science” and inspection of industry materials.