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检索条件"任意字段=Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition"

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A review of gradient-based and edge-based feature extraction methods for object detection

A review of gradient-based and edge-based feature extraction...

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11th IEEE international conference on Computer and Information Technology, CIT 2011 and 11th IEEE international conference on Scalable computing and Communications, SCALCOM 2011

作者： Wang, Sheng School of Computing and Communications University of Technology Sydney Sydney NSW Australia

ISBN: (纸本)9780769543888

In computer vision research, object detection based on image processing is the task of identifying a designated object on a static image or a sequence of video frames. Projects based on such research works have been widely adopted to various industrial and social applications. the fields to which those applications applies includes but not limited to, security surveillance, intelligent transportation system, automated manufactoring, quality control and supply chain management. In this paper, we are going to review a few most popular computer vision methods based on image processing and pattern recognition. those methods have been extensively studied in various research papers and their significance to computer vision research have been proven by subsequent research works. In general, we categorize those methods into to gradient-based and edge-based feature extraction methods, depending on the low level features they use. In this paper, the definitions for gradient and edge are extended. Because an image can also be considered as a grid of image patches, it is therefore reasonable to incorporate the concept of granules to gradient for a review. the definition for granules can be found in [1]. © 2011 IEEE.

关键词： Object detection

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A mobile agents framework supporting asynchronous access pattern in wireless networks

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11th Joint international Computer conference (JICC 2005)

作者： Xie, FS Qi, Y Hou, D Xian Jiaotong Univ Dept Comp Sci & Technol Xian 710049 Peoples R China

ISBN: (纸本)9812565329

Compared to today's traditional distributed systems, the mobile computing environment is very different in many respects. Limited battery life and high communication prices are bottlenecks in mobile computing, so a new method is urgently needed to resolve such problems. this paper proposed a new framework to meet the demands of future wireless applications. In the framework we propose Asynchronous Access pattern (AAP) and Service Configure File (SCF) supporting disconnection (include voluntarily and involuntarily) operations for mobile clients. AAP allow a mobile user voluntarily disconnect itself from the server to save its battery life and avoid high communication prices. And new wireless applications are easy to be developed/deployed based on SCF in our framework.

关键词： mobile computing mobile agents disconnection operations

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Stack Workpieces recognition Model Based on Deep Learning 11

Stack Workpieces Recognition Model Based on Deep Learning

引用

11th international conference of Information and Communication Technology, ICTech 2022

作者： Han, Weiguang Han, Xuesong University of Chinese Academy of Sciences Beijing China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781665496940

ISBN: (纸本)9781665496940

the detection and recognition of stacked workpieces is affected by workpiece occlusion and workpiece overlap, which leads to the problem of difficult detection of workpiece types. this paper proposes a detection method based on the improved Faster R-CNN model, improves the Faster R-CNN feature network, and selects ResNet combined with SENet for feature extraction, which improves the important feature layer and suppresses the non-important feature layer. Introduce the Soft-NMS algorithm to optimize the NMS algorithm to reduce the problem of missed detection and false detection of overlapping or adjacent targets. the test results show that compared with the unimproved Faster R-CNN model, the improved Faster R-CNN model outperforms the traditional algorithm in terms of accuracy, precision, recall and F1 value. © 2022 IEEE.

关键词： Feature extraction

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Particle Size Measurement based on Image Multivariate Multiscale Entropy 16

Particle Size Measurement based on Image Multivariate Multis...

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16th IEEE international conference on Trust, Security and Privacy in computing and Communications / the 11th IEEE international conference on Big Data Science and Engineering / the 14th IEEE international conference on Embedded Software and Systems

作者： Zhan, Yantong Zhang, Guoying Guo, Yu Meng, Hang Zhao, Xuejun China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing 10083 Peoples R China

ISBN: (纸本)9781509049066

An image-based multivariate multiscale entropy (MMSE) method used to measure the size of stacking particles is proposed. the intensive stacking particles with irregular shape and different sizes are collected online and processed by gradient image and one-dimensional coarse graining. Composite delay vectors are formed on row space to eliminate noise between multichannel variables. then, the multivariate multiscale entropy is calculated in order to match the particle sizes. By comparing with the state-of-the-art methods, the experiment results indicate that the method can obtain the intensive stacking particle size accurately without image segmentation.

关键词： pattern recognition Particle Size Measurement Image Multivariate Multiscale Entropy

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Mutual Cross-Attention in Dyadic Fusion Networks for Audio-Video Emotion recognition 11

Mutual Cross-Attention in Dyadic Fusion Networks for Audio-V...

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11th international conference on Affective computing and Intelligent Interaction (ACIIW)

作者： Luo, Jiachen Phan, Huy Wang, Lin Reiss, Joshua Queen Mary Univ London Ctr Digital Mus London England Amazon Alexa Cambridge MA USA

ISBN: (纸本)9798350327458

Multimodal emotion recognition is a challenging problem in the research fields of human-computer interaction and pattern recognition. How to efficiently find a common sub-space among the heterogeneous multimodal data is still an open problem for audio-video emotion recognition. In this work, we propose an attentive audio-video fusion network in an emotional dialogue system to learn attentive contextual dependency, speaker information, and the interaction of audio-video modalities. We employ pre-trained models, wav2vec, and Distract your Attention Network, to extract high-level audio and video representations, respectively. By using weighted fusion based on a cross-attention module, the cross-modality encoder focuses on the inter-modality relations and selectively captures effective information among the audio-video modality. Specifically, bidirectional gated recurrent unit models capture long-term contextual information, explore speaker influence, and learn intra- and inter-modal interactions of the audio and video modalities in a dynamic manner. We evaluate the approach on the MELD dataset, and the experimental results show that the proposed approach achieves state-of-the-art performance on the dataset.

关键词： affective computing modality fusion attention mechanism deep learning

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Sparse Support Vector Machine for pattern recognition

Sparse Support Vector Machine for pattern recognition

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2013 11th international conference on High Performance computing and Simulation, HPCS 2013

作者： Chen, Guangyi Bui, Tien. D. Krzyzak, Adam Department of Computer Science and Software Engineering Concordia University 1455 de Maisonneuve West Montreal QC H3G 1M8 Canada

ISBN: (纸本)9781479908363

Support Vector Machine (SVM) is one of the most famous classification techniques in the pattern recognition community. However, due to outliers in the training samples, the SVM tend to be over-trained. this means that the generalization ability of the SVM will decrease for further training. In this paper, we borrow the idea of compressive sensing/sparse representation and apply it to the SVM. Our method can achieve higher classification rates than the standard SVM due to the sparser support vectors. Experimental results conducted in this paper show that our proposed technique is feasible in practical pattern recognition applications. © 2013 IEEE.

关键词： Support vector machines

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Implementing Open Source Biometric Face Authentication for Multi-factor Authentication Procedures 11th

Implementing Open Source Biometric Face Authentication for M...

引用

11th international conference on High-Performance computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST)

作者： Minakova, Natalya Mansurov, Alexander Altai State Univ Lenin Ave 61 Barnaul 656049 Russia

ISBN: (纸本)9783030941413;9783030941406

this study proposes a solution that extends the capabilities of web information systems with single-factor authentication by introducing an additional authentication factor based on biometric face recognition. the proposed solution design and its main operation steps are presented and discussed. the solution utilizes the standard multimedia functionality of popular web browsers and supports available or built-in image capturing devices (photo and web cameras). Robust program algorithms from the open source computer vision library are used for face image processing and analysis. Experimental testing and validation of the algorithms for face localization and recognition are conducted with image sets produced with consideration of reality. Experimental results demonstrate high effectiveness with a success rate of 80% ... 93% for the solution based on the local binary pattern face localization algorithm with the local binary pattern histogram face recognition algorithm.

关键词： Biometrics Face authentication Face recognition Computer vision Local binary pattern

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Cloud Orchestration for Optimized computing Efficiency: the Case of Wind Resource Modelling 11

Cloud Orchestration for Optimized Computing Efficiency: The ...

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11th IEEE international conference on Cloud Networking (CloudNet)

作者： Touloupou, Marios Kapassa, Evgenia Rizou, Stamatia Singular Logic SA Europen Projects Dept Athens Greece Univ Nicosia Dept Digital Innovat Nicosia Cyprus

ISBN: (数字)9781665486279

ISBN: (纸本)9781665486279

the Weather Research and Forecasting (WRF) model is a multi-purpose open source weather model, which is widely used by the wind energy community. However, the state of the art applications that use the WRF model for wind resources do not always meet the sector's needs. the fundamental challenge is the massive quantity of data required for accurate modelling, as well as the expenses associated with the computing of such massive volumes of data, implying that state-of-the-art approaches are not appropriate. Our work tackles this issue with the use of a novel orchestration framework relying on cloud computing infrastructure. Cloud computing platforms provide robust infrastructure that allows for the deployment of production-level services as well as extended processing and storage capacity. this paper presents the overall software architecture of a Cloud Orchestration System, enabling the dynamic and flexible cloud deployment of containerized wind resource model chains. Moreover, a set of performance, functional and operational criteria are presented and evaluated, using a real use case of a wind resource model.

关键词： cloud orchestration optimal deployment computing efficiency wind resources

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proceedings - 11th IEEE/ACM international conference on Utility and Cloud computing, UCC 2018

Proceedings - 11th IEEE/ACM International Conference on Util...

引用

11th IEEE/ACM international conference on Utility and Cloud computing, UCC 2018

ISBN: (纸本)9781538655047

the proceedings contain 26 papers. the topics discussed include: hierarchical and frequency-aware model predictive control for bare-metal cloud applications;profiling and predicting application performance on the cloud;a model driven engineering approach for flexible and distributed monitoring of cross-cloud applications;pattern-based deployment models and their automatic execution;combining VM preemption schemes to improve vertical memory elasticity scheduling in clouds;pacer: automated feedback-based vertical elasticity for heterogeneous soft real-time workloads;scheduling scientific workflows on clouds using a task duplication approach;and task runtime prediction in scientific workflows using an online incremental learning approach.

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Texture image classification using unsupervised learning

Texture image classification using unsupervised learning

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11th IASTED international conference on Artificial Intelligence and Applications, AIA 2011

作者： Petrov, Nedyalko Jordanov, Ivan School of Computing University of Portsmouth PO1 3HE United Kingdom

ISBN: (纸本)9780889868632

We investigate further our intelligent machine vision system for pattern recognition and texture image classification. A database of about 335 texture images of industrial cork tiles is used for this research. the images need to be classified into several classes based on their texture features similarities. In this work, we assume that there is no a priori human vision expert knowledge about the classes. After pre-processing of the data, feature extraction and conducting statistical analysis by applying principal component analysis (PCA) and linear discriminant analysis (LDA), we investigate unsupervised neural network learning. Self-organizing map (SOM) neural networks are trained, tested and validated and the obtained results are discussed and critically compared with research works investigating similar approaches.

关键词： Feature extraction

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