A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Palm tree detection in UAV images: a hybrid approach based on multimodal particle swarm optimisation

In recent years, there has been a surge of interest in palm tree detection using unmanned aerial vehicle (UAV) images, with implications for sustainability, productivity, and profitability. Similar to other object detection problems in the field of computer vision, palm tree detection typically involves classifying palm trees from non-palm tree objects or background and localising every palm tree instance in an image. Palm tree detection in large-scale high-resolution UAV images is challenging due to the large number of pixels that need to be visited by the object detector, which is computationally costly. In this thesis, we design a novel hybrid approach based on multimodal particle swarm optimisation (MPSO) algorithm that can speed up the localisation process whilst maintaining optimal accuracy for palm tree detection in UAV images. The proposed method uses a feature-extraction-based classifier as the MPSO's objective function to seek multiple positions and scales in an image that maximise the detection score. The feature-extraction-based classifier was carefully selected through empirical study and was proven seven times faster than the state-of-the-art convolutional neural network (CNN) with comparable accuracy. The research goes on with the development of a new k-d tree-structured MPSO algorithm, which is called KDT-SPSO that significantly speeds up MPSO's nearest neighbour search by only exploring the subspaces that most likely contain the query point's neighbours. KDT-SPSO was demonstrated effective in solving multimodal benchmark functions and outperformed other competitors when applied on UAV images. Finally, we devise a new approach that utilises a 3D digital surface model (DSM) to generate high confidence proposals for KDT-SPSO and existing region-based CNN (R-CNN) for palm tree detection. The use of DSM as prior information about the number and location of palm trees reduces the search space within images and decreases overall computation time. Our hybrid approach can be executed in non-specialised hardware without long training hours, achieving similar accuracy as the state-of-the-art R-CNN

Palm tree detection in UAV images: a hybrid approach based on multimodal particle swarm optimisation

In recent years, there has been a surge of interest in palm tree detection using unmanned aerial vehicle (UAV) images, with implications for sustainability, productivity, and profitability. Similar to other object detection problems in the field of computer vision, palm tree detection typically involves classifying palm trees from non-palm tree objects or background and localising every palm tree instance in an image. Palm tree detection in large-scale high-resolution UAV images is challenging due to the large number of pixels that need to be visited by the object detector, which is computationally costly. In this thesis, we design a novel hybrid approach based on multimodal particle swarm optimisation (MPSO) algorithm that can speed up the localisation process whilst maintaining optimal accuracy for palm tree detection in UAV images. The proposed method uses a feature-extraction-based classifier as the MPSO's objective function to seek multiple positions and scales in an image that maximise the detection score. The feature-extraction-based classifier was carefully selected through empirical study and was proven seven times faster than the state-of-the-art convolutional neural network (CNN) with comparable accuracy. The research goes on with the development of a new k-d tree-structured MPSO algorithm, which is called KDT-SPSO that significantly speeds up MPSO's nearest neighbour search by only exploring the subspaces that most likely contain the query point's neighbours. KDT-SPSO was demonstrated effective in solving multimodal benchmark functions and outperformed other competitors when applied on UAV images. Finally, we devise a new approach that utilises a 3D digital surface model (DSM) to generate high confidence proposals for KDT-SPSO and existing region-based CNN (R-CNN) for palm tree detection. The use of DSM as prior information about the number and location of palm trees reduces the search space within images and decreases overall computation time. Our hybrid approach can be executed in non-specialised hardware without long training hours, achieving similar accuracy as the state-of-the-art R-CNN

NASA Tech Briefs, January 1990

Topics include: Electronic Components and Circuits. Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Fabrication Technology, Mathematics and Information Sciences, and Life Science

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

8th Annual Research Week- Event Proceedings

8th Annual Research Wee

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas