From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Deep learning for smart manufacturing: Methods and applications

Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

/pdf/state-of-the-art-in-defect-detection-based-on-machine-vision-2rtzvgsnqy.pdf

State of the Art in Defect Detection Based on Machine Vision

Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This article attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs and hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: statistical, spectral, model-based, and machine learning. These works are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

/pdf/automated-visual-defect-detection-for-flat-steel-surface-a-3wjhcxn3aa.pdf

Automated Visual Defect Detection for Flat Steel Surface: A Survey

The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges.

Modern inspection systems based on smart sensor technology like image processing and machine vision have been widely spread into several fields of industry such as process control, manufacturing, and robotics applications in factories. Machine learning for smart sensors is a key element for the visual inspection of parts on a product line that has been manually inspected by people. This paper proposes a method for automatic visual inspection of dirties, scratches, burrs, and wears on surface parts. Imaging analysis with CNN (Convolution Neural Network) of training samples is applied to confirm the defect’s existence in the target region of an image. In this paper, we have built and tested several types of deep networks of different depths and layer nodes to select adequate structure for surface defect inspection. A single CNN based network is enough to test several types of defects on textured and non-textured surfaces while conventional machine learning methods are separately applied according to type of each surface. Experiments for surface defects in real images prove the possibility for use of imaging sensors for detection of different types of defects. In terms of energy saving, the experiment result shows that proposed method has several advantages in time and cost saving and shows higher performance than traditional manpower inspection system.

Machine learning-based imaging system for surface defect inspection

Characterization of elastic moduli of Cu thin films using nanoindentation technique

An ink-jet print head preventing thermal accumulation on a nozzle plate includes a substrate, a channel formed in the substrate to supply ink, a nozzle plate connected to the substrate and including a nozzle corresponding to the channel, a heat element formed in the nozzle plate to surround the nozzle, a thermal conduction layer formed on an upper side of the heat element formed between the thermal conduction layer and the heat element, and a thermal shunt spaced-apart from the heat element by a predetermined distance not to overlap the heat element in a direction parallel to the nozzle plate and connecting the thermal conduction layer to the substrate. Redundant heat generated from the heat element is not accumulated on a membrane of the nozzle plate but is rapidly absorbed into an inorganic thermal conduction layer formed in the membrane and is transferred to the bulk silicon substrate through a metallic thermal bridge, such as the thermal shunt.

Ink-jet print head and method thereof

This paper presents the results of tensile and high cycle fatigue tests with stress ratio R = 0.1 for an Al–3% Ti thin film of 1 μm thickness in atmospheric air at room temperature. Specimens with three different widths (50, 100, and 150 μm) were fabricated to study the width effects of each sample. Test results show that tensile and fatigue properties for the Al–3% Ti thin film with different widths are very close, and, thus, width effects were found to be minimal. The elastic moduli ranged from 80 to 82 GPa, and the tensile strengths ranged from 369 to 379 MPa. Fatigue strength coefficients of the specimens with 50, 100, and 150 μm width were 193, 181, and 164 MPa, respectively. In addition, fatigue strength exponents of the specimens with 50, 100, and 150 μm width were −0.023, −0.020, and −0.013, respectively. When present test results are compared with typical properties of bulk aluminium, the Al–3% Ti thin film is found to have longer life at the same stress, but it is more sensitive to the stress level.

Tensile and high cycle fatigue test of Al–3% Ti thin films

This paper presents the results of tensile and high cycle fatigue tests for copper thin film Copper films coated by Sn are often used in various electro devices Especially, when the film is used in tape carrier package (TCP), the film is repeatedly exposed to mechanical or(and) thermal stresses which results often in the failure of the component Therefore, to guarantee the reliability of the electrical devices using a film, tensile and fatigue characteristics of the film are important In this study, to obtain the tensile and fatigue characteristics of the film, the specimen was fabricated by an etching process to make a smooth specimen of 2000 μm width, 8000 μm length and 1526 μm thickness The tensile and high cycle fatigue tests were performed with the specimen using the test machine developed by the authors These specimens had measured values of Young's modulus (72 GPa) and a 02 % offset yield and an ultimate strength of 358 MPa and 462 MPa, respectively A closed-loop feedback control of the magnetic-electric actuator allowed load-controlled fatigue tests with 20 Hz frequency, in ambient environment and at two levels of mean stresses The fatigue strength coefficient and exponent at 05 times of the ultimate tensile strength were 431 MPa and -00843, respectively The fatigue strength coefficient and exponent at 06 times of the ultimate tensile strength were 371 MPa and -00923, respectively The Goodman method is recommended when the fatigue life of thin film with mean stress will be estimated The fatigue strength coefficient and exponent of copper thin film modified using Goodman method were 910 MPa and -00896, respectively

Tensile and high cycle fatigue test of copper thin film

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