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Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： De-Shuang Huang Donald C. Wunsch Daniel S. Levine Kang-Hyun Jo

ISBN: (数字)9783540874423

ISBN: (纸本)9783540874409

The international conference on Intelligent Computing (ICIC) was formed to p- vide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and ind- try to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing. ICIC 2008, held in Shanghai, China, September 15–18, 2008, constituted the 4th international conference on Intelligent Computing. It built upon the success of ICIC 2007, ICIC 2006 and ICIC 2005 held in Qingdao, Kunming and Hefei, China, 2007, 2006 and 2005, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Emerging Intelligent Computing Technology and Applications”. Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology.

关键词： Programming Techniques Computer Applications pattern recognition Computation by Abstract Devices Algorithm Analysis and Problem Complexity Artificial intelligence

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HCI international 2021 - Posters 1

引用

丛书名： Communications in Computer and Information Science

1000年

作者： Constantine Stephanidis Margherita Antona Stavroula Ntoa

ISBN: (数字)9783030786359

ISBN: (纸本)9783030786342

The three-volume set CCIS 1419, CCIS 1420, and CCIS 1421 contains the extended abstracts of the posters presented during the 23rd international conference on Human-Computer Interaction, HCII 2021, which was held virtually in July 2021.;The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions.

关键词： User Interfaces and Human Computer Interaction Artificial intelligence Information Systems Applications (incl. Internet) Computer Systems Organization and Communication Networks Software Engineering/Programming and Operating Systems Computer Imaging, Vision, pattern recognition and Graphics

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3rd international conference on Algebraic Algorithms and Error-Correcting Codes, AAECC 1985

3rd International Conference on Algebraic Algorithms and Err...

引用

3rd international conference on Algebraic Algorithms and Error-Correcting Codes, AAECC 1985

ISBN: (纸本)9783540167761

The proceedings contain 44 papers. The special focus in this conference is on Algebraic Algorithms and Error-Correcting Codes. The topics include: Algebraic methods for constructing asymmetric cryptosystems;covering radii of even subcodes of t-dense codes;orthogonal transform encoding of cyclic codes;on s-sum-sets and projective codes;pseudo-triple-sum-sets and association schemes;a decoding algorithm for linear codes;the finite Fourier-transform and theta functions;recent results on coding and algebraic geometry;some properties of elliptic codes over a field of characteristic 2;on the arithmetics of Galoisfields and the like: Algebraic questions arising in the design of secure communication systems;self-dual codes 2n circulant over Fq(q=2r);automorphisms and isometries of some modular algebras;a lower bound for the bilinear complexity of some semisimple lie algebras;on computational complexity of some algebraic curves over finite fields;some group presentations and enforcing the associative law;fast computation of linear finite-dimensional operators over arbitrary rings;quantifier elimination for real closed fields;efficient decision algorithms for locally finite theories;the algorithmic structure of sl(2,k);optimal algorithms for finite dimensional simply generated algebras;on strongly tactical codes;on a little but useful algorithm;computation of independent units in number fields by Dirichlet's method: Extended abstract;some upper bounds for the multiplicity of an autoreduced subset of Nmand their applications;exact computation of the characteristic polynomial of an integer matrix;an analysis of the Krönecker algorithm for factorization of algebraic polynomials;polynomial factorization over [X];the L-machine: An attempt at parallel hardware for symbolic computation.

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Deep Learning in Image Processing: Revolutionizing Diagnosis in the Field of Dermatology 3rd

Deep Learning in Image Processing: Revolutionizing Diagnosis...

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3rd international conference on Advanced Informatics for Computing Research (ICAICR)

作者： Nayak, Richa Hasija, Yasha Delhi Technol Univ Bawana Rd Delhi India

ISBN: (纸本)9789811501081;9789811501074

The modern-day society is increasingly dependent on computer-aided tools and techniques. Digital imaging techniques have a tremendous impact on our day-to-day lives. Image processing is a vital component in the field of biological sciences and has the potential to drastically change the computer-human interface. Image processing refers to the conversion of an image into a digital form followed by enhancement of the image in order to extract useful information from it that are indiscernible by human ocular perceivers. Rapid advances in image processing, computerized reconstruction of an image and allied advancements in image analysis algorithms and the application of artificial intelligence has spurred a revolution in the field of medical and diagnostic imaging. Deep learning, a type of Artificial Neural Network (machine Learning), is resurfacing as a powerful tool for its utilization in big healthcare data. The integration of deep learning techniques to image processing has the potential to add momentum to the dermatological imaging and promote early and accurate diagnosis of skin lesions. This review attempts to discuss the fundamentals of image processing, its importance, various clinical imaging modalities in use in the field of dermatology and application of deep learning algorithms in dermatological imaging, accentuating the inadequacies and future research prospects.

关键词： Image processing Image segmentation Dermatological imaging Deep learning

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Indoor Scene Classification Algorithm Based on an Object Vector for Robot Applications 2022

Indoor Scene Classification Algorithm Based on an Object Vec...

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3rd international conference on Artificial intelligence in Electronics Engineering, AIEE 2022

作者： Fan, Ximing Zhu, Bo Gao, Xiang Wang, Bin Wang, Cong Xu, Guozheng School of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications China China Unicom Internet of Things Corporation Limited Nanjing City Jiangsu Province China

ISBN: (纸本)9781450395489

At present, many indoor scene classification algorithms are based on abstract feature descriptions and lack semantic interpretation for classification results. For robot applications, these classification methods fail to reuse prior knowledge of environmental objects, resulting in a waste of computing resources, and are not conducive to interaction. In addition, a large number of existing methods have not been verified for robot applications because training and test are performed on the same dataset. Here, intermediate semantic structure, called object vector, is proposed to describe the scene. The recognition confidence, category, and quantity of typical objects, which are related to scene concepts, can be captured by this structure. Based on the object vector, the semantic prototype models of scene categories are obtained using the prototype learning method, and the corresponding prototype confidence vector is generated for scene prediction. The object vector and prototype confidence vector are concatenated to form a complete descriptor. Applying sparse random forest learning, scene conceptual models are formed and used in real scene classification. In the experiment, the training and test sets were selected from sets with different styles. The average accuracy on the MIT Indoor 67, VPC, and Tsotsos_home datasets is higher than that of fine-tuned VGG. In particular, the accuracy on the VPC dataset improved by more than 5%. Moreover, the algorithm performance is evaluated on an actual robot platform. © 2022 ACM.

关键词： Vectors

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Image-Based 3D Ultrasound Reconstruction with Optical Flow via Pyramid Warping Network 43

Image-Based 3D Ultrasound Reconstruction with Optical Flow v...

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43rd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Xie, Yanting Liao, Hongen Zhang, Daoqiang Zhou, Lei Chen, Fang Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Engn MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing 210016 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 10084 Peoples R China

ISBN: (纸本)9781728111797

3D Ultrasound (US) contains rich spatial information which is helpful for medical diagnosis. However, current reconstruction methods with tracking devices are not suitable for clinical application. The sensorless freehand methods reconstruct based on US images which is less accuracy. In this paper, we proposed a network which reconstructs the US volume based on US images features and optical flow features. We proposed the pyramid warping layer which merges the image features and optical flow features with warping operation. To fuse the warped features of different scales in different pyramid levels, we adopted the fusion module using the attention mechanism. Meanwhile, we adopted the channel attention and spatial attention to our network. Our method was evaluated in 100 freehand US sweeps of human forearms which exhibits the efficient performance on volume reconstruction compared with other methods.

关键词： Ultrasonic imaging Three-dimensional displays Fuses Reconstruction algorithms Trajectory Medical diagnosis Task analysis

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Federated Generative Adversarial Learning 3rd

Federated Generative Adversarial Learning

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3rd Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Fan, Chenyou Liu, Peng Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China ASTAR Inst High Performance Comp IHPC Singapore Singapore

ISBN: (纸本)9783030606350;9783030606367

This work studies training generative adversarial networks under the federated learning setting. Generative adversarial networks (GANs) have achieved advancement in various real-world applications, such as image editing, style transfer, scene generations, etc. However, like other deep learning models, GANs are also suffering from data limitation problems in real cases. To boost the performance of GANs in target tasks, collecting images as many as possible from different sources becomes not only important but also essential. For example, to build a robust and accurate bio-metric verification system, huge amounts of images might be collected from surveillance cameras, and/or uploaded from cellphones by users accepting agreements. In an ideal case, utilize all those data uploaded from public and private devices for model training is straight-forward. Unfortunately, in the real scenarios, this is hard due to a few reasons. At first, some data face the serious concern of leakage, and therefore it is prohibitive to upload them to a third-party server for model training;at second, the images collected by different kinds of devices, probably have distinctive biases due to various factors, e.g., collector preferences, geo-location differences, which is also known as "domain shift". To handle those problems, we propose a novel generative learning scheme utilizing a federated learning framework. Following the configuration of federated learning, we conduct model training and aggregation on one center and a group of clients. Specifically, our method learns the distributed generative models in clients, while the models trained in each client are fused into one unified and versatile model in the center. To the best of our knowledge, this is the first work on touching GAN training under a federated learning setting. We perform extensive experiments to compare different federation strategies, and empirically examine the effectiveness of federation under different levels of parallel

关键词： Federated learning Generative Adversarial Network Non-IID data

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Fast 3D hand estimation for mobile interactions

Fast 3D hand estimation for mobile interactions

引用

international conference on pattern recognition

作者： Yuru Pei Gengyu Ma Machine Intelligence Department Peking University Beijing China uSens Inc San Jose CA USA

ISBN: (纸本)9781509048489

The ubiquitous hand gesture plays an important role in the natural human machine interaction (HMI). Recently, the consumer color and depth cameras have been used to estimate hand shapes and postures for the mid-air HMI. Under the observation that 3D hand contours possess much information of hand postures, we estimate 3D hand contours from infrared images with a limited computation complexity for the HMI on mobile devices. A variant of the dynamic programming (vDP) algorithm is proposed to handle complex self-occlusions in 3D hand estimations, where a set of heuristic rules are introduced to avoid finger missing. Furthermore, the constraints are used to reduce the searching space in contour alignments. Given 3D hand contours, a set of hand gestures, including touching, swiping, and pinching, can be applied to mid-air interactions. The proposed method is much faster than the traditional depth estimation of the whole hand, and can achieve up to 500 Hz on PC, and 100 Hz on mobile devices.

关键词： Three-dimensional displays Estimation Cameras Mobile handsets Shape Mobile communication Reliability

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Intelligent System for the Reduction of Injuries in Archery 3rd

Intelligent System for the Reduction of Injuries in Archery

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3rd international conference on Optimization and Learning (OLA)

作者： Cintrano, Christian Ferrer, Javier Alba, Enrique Univ Malaga ETSI Informat Bulevar Louis Pasteur 35 Malaga 29071 Spain

ISBN: (纸本)9783030419134;9783030419127

Archery is one of these sports in which the athletes repeat the same body postures over and over again. This means that tiny wrong habits could cause serious long-term health injuries. Consequently, learning a correct shooting technique is very important for both beginner archers and elite athletes. In this work, we present a system that uses machine learning to automatically detect anomalous postures and return to the archer a shooting score, that works by giving the archer a feedback on his own body configuration. We use a neural network to analyze images of archers during the firing and return the place of their different body joints. With this information, the system can detect wrong postures which might lead to injuries. This feedback is very important to the archer when learning the shooting technique. In addition, the system is not intrusive for the archer, so she/he can fire arrows freely. Preliminary results show the usefulness of the system, which is able to detect 4 spine misalignment and 4 raised elbow analyzing only 9 shots.

关键词： Improved sports performance Injury reduction Artificial intelligence machine learning Body posture analysis

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Towards improving the accuracy of sensorless freehand 3D ultrasound by learning

Towards improving the accuracy of sensorless freehand 3D ult...

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3rd international Workshop on machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012

作者： Conrath, Juliette Laporte, Catherine Dept. of Electrical Engineering École de Technologie Supérieure Montreal QC Canada

ISBN: (纸本)9783642354274

Sensorless freehand 3D ultrasound (US) exploits the correlation between pairs of images in order to track the rigid motion of the US probe without an external position sensor. Conventionally, in-plane motion is compensated by maximizing image correlation. Out-of-plane motion is then estimated using a calibrated model of elevational speckle decorrelation. This approach is prone to systematic error due to interactions between the effects of in-plane and out-of-plane motion components on speckle decorrelation. This paper proposes to establish an error correction model using relevance vector regression and a database of image pairs with known probe motion. Preliminary results on synthetic US image pairs show a statistically significant reduction in mean target registration error, illustrating the promise of the new approach. © 2012 Springer-Verlag.

关键词： Ultrasonics

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