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检索条件"任意字段=Intelligent Robotics and Industrial Applications using Computer Vision 2017, IRIACV 2017"

共 126 条记录，以下是61-70 订阅

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Part quality assessment using convolution neural networks in high pressure die casting

Part quality assessment using convolution neural networks in...

引用

intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Cashion, Kelly Powar, Nilesh De Neff, Robert Kress, Robert University of Dayton Research Institute DaytonOH United States Honda North America MarysvilleOH United States Honda Transmission Manufacturing Russells PointOH United States

High pressure die casting (HPDC) has been developed since the late nineteenth century for a breadth of manufacturing applications. The process forms molten metal into molds at high temperatures given a complex array of parameters and variables that are challenging to observe. We used a set of thermal cameras to capture imagery of the die used as a mold during its cooling process between part productions. This data was used to train a convolution neural network to assess the quality of the part just produced based on the thermal characteristics of the surface of the die. The system achieved 90% accuracy when distinguishing between parts that met quality standards and parts that did not. © 2018, Society for Imaging Science and Technology.

关键词： Molds

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An image processing based method for chewing detection using Variable-intensity template

An image processing based method for chewing detection using...

引用

intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Fujimoto, Atsuto Ohkawauchi, Takaaki Yamato, Junji Ohya, Jun Department of Modern Mechanical Engineering Waseda University Tokyo Japan

To contribute to the actualization of the care worker assistance robot, this paper proposes a method for detecting whether the care receiver is chewing from the video sequence acquired by the camera that observes that receiver. The proposed method detects the receiver's face and areas for both cheeks and chin. After applying some normalization to the areas, chewing detection that uses a variable-intensity template is performed, where the template consists of shape models, interest points and intensity distribution model. A likelihood based on the variableintensity template is computed so that the receiver is judged whether the receiver is chewing. Experiments using seven subjects are conducted. As a result, the accuracy of chewing detection by the proposed method is 83%, which is quite promising. © 2018, Society for Imaging Science and Technology.

关键词： Image processing

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Featureless-Region-Based top window recognition for automatic industrial monitoring systems

Featureless-Region-Based top window recognition for automati...

引用

intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Ho, Mei-Hsuan Tsai, Yu-Hsuan Hsu, Chang-Tao Chuang, Ren-Hui Dept. of Computer Science and Information Engineering National Chaio Tung University Taiwan

As a vital part for industrial automatic monitoring systems, this paper presents the algorithm for top window recognition on screen-grabbed images. For better performance in terms of accuracy, efficiency, and robustness, we introduce a novel, background-based image feature of window region, i.e., locallymaximum uniform blocks extracted from regions of uniform color, to represent the practically unique spatial structure of background regions in different windows. The recognition procedure consists of several rather simple steps, c.f., foreground-based methods, which include (i) edge extraction, (ii) uniform block extraction, (iii) uniform block matching. The template window with highest matching score is then identified the top window, with its location determined simultaneously to facilitate further analysis of window content. Experimental results indicate that the proposed approach can achieve perfect recognition results for realistic image data from the industry, and also fulfill the near real-time processing requirement of on-line monitoring tasks. © 2018, Society for Imaging Science and Technology.

关键词： Extraction

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Adaptive planar vision marker composed of LED arrays for sensing under low visibility 5th

Adaptive planar vision marker composed of LED arrays for sen...

引用

5th International Conference on Robot Intelligence Technology and applications, RiTA 2017

作者： Kim, Kyukwang Hyun, Jieum Myung, Hyun Urban Robotics Laboratory Korea Advanced Institute of Science and Technology 291 Daehak-ro Daejeon34141 Korea Republic of

ISBN: (纸本)9783319784519

In image processing and robotic applications, two-dimensional (2D) black and white patterned planar markers are widely used. However, these markers are not detectable in low visibility environment and they are not changeable. This research proposes an active and adaptive marker node, which displays 2D marker patterns using light emitting diode (LED) arrays for easier recognition in the foggy or turbid underwater environments. Because each node is made to blink at a different frequency, active LED marker nodes were distinguishable from each other from a long distance without increasing the size of the marker. We expect that the proposed system can be used in various harsh conditions where the conventional marker systems are not applicable because of low visibility issues. The proposed system is still compatible with the conventional marker as the displayed patterns are identical. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Light emitting diodes

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Depth map inpainting using a fully convolutional network

Depth map inpainting using a fully convolutional network

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Xiong, Rong Liu, Guodong Qu, Yangyang Ou, Yongsheng 1068 Xueyuan Blvd Shenzhen China Shenzhen College of Advanced Tech-nology University of Chinese Academy of Sciences 1068 Xueyuan Blvd Shenzhen China Guangdong Provin-cial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences 1068 Xueyuan Blvd Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology 1068 Xueyuan Blvd Shenzhen China

ISBN: (纸本)9781728163215

Emerging consumer depth cameras, such as Microsoft Kinect and ASUS Xtion Pro, are low-cost depth sensors which can capture the depth maps of the scene, and obtaining color images of the corresponding scenes at the same time. Depth information is very important for computer vision applications, and to acquire the accurate depth information is challengeable. However, due to the limitations of imaging principle, depth maps acquired by such devices are often noisy and will lose depth values for certain areas. In this paper, we present a novel method for depth inpainting problem using a Fully Convolutional Network (FCN). The network has two input branches. One branch is a color image that has been processed by a convoluted hidden layer, and the other one is the missing depth map. Besides, we add a shortcut in order to improve the feature extraction capability of the network. We have validated our method in the Middlebury Stereo Dataset and real-world scenes captured by Kinect 2.0. Experiments show that our method has better applicability and reliability than traditional methods in depth inpainting. © 2019 IEEE.

关键词： Convolution

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using shape descriptors for uav detection

Using shape descriptors for uav detection

引用

intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Unlu, Eren Zenou, Emmanuel Riviere, Nicolas ISAE-SUPAERO Toulouse France ONERA Toulouse France

The rapid development of Unmanned Aerial Vehicle (UAV) technology, -also known as drones- has raised concerns on the safety of critical locations such as governmental buildings, nuclear stations, crowded places etc. computer vision based approach for detecting these threats seems as a viable solution due to various advantages. We envision an autonomous drone detection and tracking system for the protection of strategic locations. It has been reported numerous times that, one of the main challenges for aerial object recognition with computer vision is discriminating birds from the targets. In this work, we have used 2-dimensional scale, rotation and translation invariant Generic Fourier Descriptor (GFD) features and classified targets as a drone or bird by a neural network. For the training of this system, a large dataset composed of birds and drones is gathered from open sources. We have achieved up to 85.3% overall correct classification rate. © 2018, Society for Imaging Science and Technology.

关键词： Drones

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Reliable primitive approximation for estimation of robot grasping parameters using 3D-DNN

Reliable primitive approximation for estimation of robot gra...

引用

intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Torii, Takuya Hashimoto, Manabu Chukyo University Aichi Japan

In automatic picking by robot, it is the important to estimate the grasping parameters (grasping position, direction and angle) of the object. In this paper, we propose a method for approximating an object with primitive shape in order to estimate the grasping parameters. The basic idea of this research is to approximate the object by object primitive (hexahedron/cylinder/sphere), based on the object's surface. First, we classify the surface shape that constitutes the object using 3D-Deep Neural Network. Then, we approximate the object with object primitive using the recognition result of 3D-DNN. After that, we estimate the grasping parameters based on preset grasping rules. The success rate of approximating the object primitive with our method was 94.7%. This result is 6.7% higher than the 3D ShapeNets using 3DDNN. Also, as an experimental result of grasping simulation using Gazebo, the success rate of grasping with our method was 85.6%. © 2018, Society for Imaging Science and Technology.

关键词： Deep neural networks

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Multi-view surface inspection using a rotating table

Multi-view surface inspection using a rotating table

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intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Kaichi, Tomoya Mori, Shohei Saito, Hideo Sugano, Junichi Adachi, Hideyuki Keio University Kanagawa Japan Visco Technologies Corporation Tokyo Japan

In this paper, we introduce a method to visually inspect in- dustrial parts by using multi-view images of an industrial part on a rotating table. During the visual inspection of the parts' sur- faces, the relationship between the camera pose, the light vectors, and the normal vectors of a part's surface is the key factor in de- tecting abnormalities. We can change the relationship between the three factors by rotating the part;then, the abnormalities are visible in certain positional relationships. We, therefore, track the points on a part's surfaces in an image sequence and discrimi- nate abnormal points (such as points on scratches or dents) from normal points based on pixel value transitions while rotating the part. The experimental results, which were based on real data of industrial parts, showed that the proposed method could detect the abnormalities on the surfaces. © 2018, Society for Imaging Science and Technology.

关键词： Inspection

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Bringing machine intelligence to welding visual inspection: Development of Low-Cost portable embedded device for welding quality control

Bringing machine intelligence to welding visual inspection: ...

引用

intelligent robotics and industrial applications using computer vision 2018, iriacv 2018

作者： Gong, Yifu Lin, Zhibin Wang, Jinhui Gong, Na Department of Electrical and Computer Engineering North Dakota State University FargoND United States Department of Civil and Environmental Engineering North Dakota State University FargoND United States

Welding is commonly used for connecting metal components in these critical metallic infrastructure, such as agricultural facilities, wind turbines, railways, bridges and pipelines. However, welding processes vulnerably lead to forming cracks, pores, and other defects on the surface. These defects not only could result in severer cracks and corrosion, but also may ultimately lead to malfunction and failure of metal components. Inspection of welds is thus critical to ensure the welding quality during fabrication, construction process, and later in-service stage. The visual inspection is the crucial and most cost-effective step to determine if the welding quality is passed or rejected. However, fast and accurately determining welding quality is a challenging task in the conventional visual inspection process, which is highly dependent on the experience and expertise of inspectors, and it is fairly subjective and sometimes even misleading. To meet the gap, we bring machine intelligence to welding visual inspection. Specifically, we developed a low-cost portable embedded device to support advanced machine learning algorithms for real-time welding image processing. © 2018, Society for Imaging Science and Technology.

关键词： Learning algorithms

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Semi-supervised representation learning for infants biliary atresia screening using deep CNN-based variational autoencoder

Semi-supervised representation learning for infants biliary ...

引用

International Conference on Mechatronics and intelligent robotics, ICMIR 2018

作者： Wan, Zhiyong Qin, Wenjian Song, Kuiliang Wang, Bin Zhang, Dahao Li, Ling Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China Shandong Zhongbaokang Medical Implements Co. LTD Zibo China ShenZhen Children’s Hospital China Shenzhen China

ISBN: (纸本)9783030002138

The challenge of convolutional networks (CNNs) for medical imaging analysis is to train the network model with limited well labeled dataset. Since a variational autoencoder (VAE) is able to learn the probability distribution on data for describing an observation in terms of its latent attributes by unsupervised manner, it has emerged as one of the most popular unsupervised learning technology in computer vision applications. In this paper, we presented a semi-supervised representation learning approach for screening infant’s biliary atresia using convolutional variational autoencoder (CVAE). Firstly, we leveraged a smartphone’s camera for infant’s stool images collection. Secondly, a pre-trained deep convolutional variation autoencoder was used to train the feature extractor for the infant’s stool image-features extraction, and finally we fine-tuned the last classification layers for identifying acholic from normal stools. We compared our screening approach with "tradition stool color card" method;the results demonstrated CVAE model has a higher accuracy rate 92.16%. Universal screening biliary atresia by semi-supervised representation learning may be a valuable technology to help parents identify acholic stools in the perinatal period, which may ultimately lead to improved native liver survival probabilities. © Springer Nature Switzerland AG 2019.

关键词： Medical imaging

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