This study presents a deep-learning (DL) methodology using 3-D convolutional neural networks (CNNs) to detect defects in carbon fiber-reinforced polymer (CFRP) composites through volumetric ultrasonic testing (UT) dat...
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This study presents a deep-learning (DL) methodology using 3-D convolutional neural networks (CNNs) to detect defects in carbon fiber-reinforced polymer (CFRP) composites through volumetric ultrasonic testing (UT) data. Acquiring large amounts of ultrasonic training data experimentally is expensive and time-consuming. To address this issue, a synthetic data generation method was extended to incorporate volumetric data. By preserving the complete volumetric data, complex preprocessing is reduced, and the model can utilize spatial and temporal information that is lost during imaging. This enables the model to utilize important features that might be overlooked otherwise. The performance of three architectures was compared. The first architecture is prevalent in the literature for the classification of volumetric datasets. The second demonstrated a hand-designed approach to architecture design, with modifications to the first architecture to address the challenges of this specific task. A key modification was the use of cuboidal kernels to account for the large aspect ratios seen in ultrasonic data. The third architecture was discovered through neural architecture search (NAS) from a modified 3-D residual neural network (ResNet) search space. In addition, domain-specific augmentation methods were incorporated during training, resulting in significant improvements in model performance, with a mean accuracy improvement of 22.4% on the discovered architecture. The discovered architecture demonstrated the best performance with a mean accuracy increase of 7.9% over the second-best model. It was able to consistently detect all defects while maintaining a model size smaller than most 2-D ResNets. Each model had an inference time of less than 0.5 s, making them efficient for the interpretation of large amounts of data. [GRAPHICS]
A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters during search. Experiments show that CCA...
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A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters during search. Experiments show that CCA-AAMs, while requiring similar implementation effort, consistently outperform standard search with regard to convergence speed by a factor of four.
Some of these so-called AIoT applications include intelligent imageprocessing in smart factories to monitor machinery conditions and control raw material inventory, identifying abnormalities in medical images, and au...
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Some of these so-called AIoT applications include intelligent imageprocessing in smart factories to monitor machinery conditions and control raw material inventory, identifying abnormalities in medical images, and automatic real-time scanning and recognition of license plates in traffic to locate stolen cars.
The topics covered in this special issue include (i) intelligent imageprocessing applications and services to fulfill the real-time processing and performance demands, (ii) real-time deep learning and machine learning solutions to improve computational speed and increase recognition rates at network edges, (iii) new frameworks to optimize real-time AIoT imageprocessing, and (iv) combining intelligent real-time imageprocessing with edge computing, fog computing, and relevant techniques to balance the computational workloads between IoT devices and the server side.
Fan and Guan [1] have developed a deep face verification framework based on SIFT (scale invariant feature transform) and CNN (convolutional neural network) methods.
Real-Time Simulation of Accommodation and Low-Order Aberrations of the Human Eye using Light-Gathering Trees.Occlusion-Based Point Cloud Exploration Using a Linear-Time Structure.Evaluating virtual reality locomotion ...
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Real-Time Simulation of Accommodation and Low-Order Aberrations of the Human Eye using Light-Gathering Trees.
Occlusion-Based Point Cloud Exploration Using a Linear-Time Structure.
Evaluating virtual reality locomotion interfaces on collision avoidance task with a virtual character.
International program committee Yiannis Aloimonos, University of Maryland, United States Andreas Aristidou, University of Cyprus, Cyprus Selim Balcisoy, Sabanci University, Turkey Loic Barthe, IRIT – Université de Toulouse, France Sebastiano Battiato, University of Catania, Italy Jan Bender, RWTH Aachen University, Germany Bedrich Benes, Purdue University, United States Werner Benger, AHM Software GmbH, Austria Stefan Bruckner, University of Bergen, Norway Katja Bühler, VRVis, Austria Y Cai, Nanyang Technological University, Singapore Tolga Capin, TED University, Turkey Jian Chang, Bournemouth University, United Kingdom Falai Chen, Department of Mathematics, University of Science and Technology of China, China Jie Chen, Hong Kong Baptist University, Singapore Jie Chen, University of Oulu, Finland Renjie Chen, University of Science and Technology of China, China David Coeurjolly, CNRS – LIRIS, France Frederic Cordier, Université de Haute Alsace, France Massimiliano Corsini, University of Modena and Reggio Emilia, Italy Rémi Cozot, LISIC, France Naser Damer, Fraunhofer, Germany Zhigang Deng, University of Houston, United States Amal Dev P, TU Delft, Netherlands Jean-Michel Dischler, University of Strasbourg, France Parris Egbert, Brigham Young University, United States Petros Faloutsos, York University, Canada Bin Fan, University of Science and Technology Beijing, China Jieqing Feng, Zhejiang University, China Ioannis Fudos, University of Ioannina, Greece Issei Fujishiro, Keio University, Japan Xifeng Gao, Florida State University, United States Christoph Garth, Technische Universität Kaiserslautern, Germany Marina Gavrilova, University of Calgary, Canada Enrico Gobbetti, CRS4 Visual Co
Hyper-Resolution, a new technique for super-resolution reconstruction of images, is based on matching low-resolution target image details to their high-resolution counterparts from an image database. Central to the al...
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Hyper-Resolution, a new technique for super-resolution reconstruction of images, is based on matching low-resolution target image details to their high-resolution counterparts from an image database. Central to the algorithm is a novel transform of image content from the orthogonal pixel space to a parametric space structured around edges. This approach offers improved quality, more flexibility and significantly faster performance than previous work in the field. Implementation strategies for achieving this efficiency are carefully outlined. The algorithm is evaluated by controlled assessment, qualitative evaluation, and applications to facial detail reconstruction and identification. The algorithm is finally analyzed through the comparison with alternative techniques. (c) 2005 Elsevier Ltd. All rights reserved.
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