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检索条件"任意字段=4th International Conference on Embedded and Multimedia Computing"

共 1513 条记录，以下是11-20 订阅

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AF Mask R-CNN: Attention Fused Mask R-CNN Mechanism for Instance Segmentation 4th

AF Mask R-CNN: Attention Fused Mask R-CNN Mechanism for Inst...

引用

4th international conference on Smart multimedia, ICSM 2024

作者： Su, Zhichao Leng, Chengcai Cheng, Irene School of Mathematics Northwest University Xi’an710127 China Department of Computing Science University of Alberta EdmontonABT6G 2E8 Canada

ISBN: (纸本)9783031824746

Target detection tasks need to incorporate scene semantic understanding in order to achieve more precise instance segmentation. Instance segmentation is a challenging research task but is fundamental in many applications. In this paper, we propose an Attention Fused Mask R-CNN Mechanism (AF Mask R-CNN), which is based on a dual attention mechanism, and is designed for detecting and segmenting instances in visible images making use of semantic information. the experimental results obtained using the Coco 2017 dataset show that our AF Mask R-CNN model can effectively improve the segmentation accuracy. We are able to demonstrate that AF Mask R-CNN outperforms current instance segmentation deep learning algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Two-Stage Synthetic Supervising and Multi-view Consistency Self-supervising Based Animal 3D Reconstruction by Single Image 4th

Two-Stage Synthetic Supervising and Multi-view Consistency ...

引用

4th international conference on Smart multimedia, ICSM 2024

作者： Kuang, Zijian Ying, Lihang Jin, Shi Cheng, Li Department of Computing Science University of Alberta Edmonton Canada Zerobox Inc. Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (纸本)9783031824746

Pixel-aligned Implicit Function (PIFu) effectively captures subtle variations in body shape within a low-dimensional space through extensive training with human 3D scans, its application to live animals presents formidable challenges due to the difficulty of obtaining animal cooperation for 3D scanning. To address this challenge, we propose the combination of two-stage supervised and self-supervised training to address the challenge of obtaining animal cooperation for 3D scanning. In the first stage, we leverage synthetic animal models for supervised learning. this allows the model to learn from a diverse set of virtual animal instances. In the second stage, we use 2D multi-view consistency as a self-supervised training method. this further enhances the model’s ability to reconstruct accurate and realistic 3D shape and texture from largely available single-view images of real animals. the results of our study demonstrate that our approach outperforms state-of-the-art methods in both quantitative and qualitative aspects of bird 3D digitization. the source code is available at https://***/kuangzijian/drifu-for-animals. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D reconstruction

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Entropy Based embedded Unsupervised Feature Selection 4th

Entropy Based Embedded Unsupervised Feature Selection

引用

4th international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2024

作者： Rastogi, Reshma Aich, Era Lab South Asian University Delhi India

ISBN: (纸本)9783031837920

Machine-learning techniques often encounter significant challenges when dealing with high-dimensional data due to the substantial memory requirements and extended processing times involved. In this paper, we introduce a novel unsupervised feature selection method called Entropy based embedded Unsupervised Feature Selection (EEUFS). this method aims to select a suitable and concise subset of features while minimizing redundancy by assessing the entropy of each feature. computing entropy for each feature, evaluating correlation between features and eliminating redundant ones, our approach reduces the feature set size while retaining essential information from the original dataset. Experimental evaluations conducted across six datasets using six different entropy measures demonstrate that our proposed Entropy based embedded Unsupervised Feature Selection(EEUFS) method is effective and often outperforms state-of-the-art unsupervised feature selection methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Parallel Computation : 4th international Acpc conference Including Special Tracks on Parallel Numerics (Parnum'99) and Parallel computing in Image Processing, Video Processing, and multimedia, Salzburg, Austria, February 16-18, 1999 : Proceedings

引用

2016年

作者： peter zinterhof

Parallel Computation : 4th international Acpc conference Including Special Tracks on Parallel Numerics (Parnum'99) and Parallel computing in Image Processing, Video Processing, and multimedia, Salzburg, Austria, February 16-18, 1999 : Proceedings by international Acpc conference (4th : 1999 : Salzburg, Austria); Zinterhof, Peter; Vajteršic, Marián; Uhl, Andreas, 1968-; published by Berlin ; New York : Springer

关键词： (parnum'99) austria, parallel parallel processing (electronic computers) processing,

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Concept Factorization with Manifold and Orthogonal Constraint for Data Representation 4th

Concept Factorization with Manifold and Orthogonal Constrain...

引用

4th international conference on Smart multimedia, ICSM 2024

作者： Zhang, Hui Wu, Haonan Leng, Chengcai Li, Ning Cheng, Irene School of Mathematics and Statistics Xianyang Normal University Xianyang712000 China School of Mathematics Northwest University Xi’an710127 China Department of Computing Science University of Alberta EdmontonABT6G 2E8 Canada

ISBN: (纸本)9783031824746

Low rank matrix factorization techniques, such as Non-negative Matrix Factorization (NMF) and Concept Factorization (CF), have attracted considerable attention in data analysis. However, both of them effectively only exploit the global Euclidean geometry, without fully considering the local manifold geometry. In this paper, we propose a novel sparse matrix factorization method for data clustering called Concept Factorization with Manifold and Orthogonal Constraint for Data Representation (CF-MO), which considers the geometric structures of the data manifold along with the sparse orthogonal constraint. CF-MO can discover the intrinsic geometric structure of the data, and lead to rigorous clustering interpretation by minimizing the redundancy between different orthogonal bases. Furthermore, we develop the iterative update rules for CF-MO, and prove the convergence of this algorithm. Experimental results demonstrate the effectiveness and performance of our proposed method compared to state-of-the-art algorithms on real-world images. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

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PECCS 2014 - Proceedings of the 4th international conference on Pervasive and embedded computing and Communication Systems

PECCS 2014 - Proceedings of the 4th International Conference...

引用

4th international conference on Pervasive and embedded computing and Communication Systems, PECCS 2014

ISBN: (纸本)9789897580000

the proceedings contain 33 papers. the topics discussed include: scalable algorithm design for high-level synthesis and optimization of latency insensitive systems;weighted sum-rate maximization for multi-user mimo-OFDM downlink with ZF-DPC methods;urban scale dissemination in mobile pervasive computing environments;measured firmware deployment for embedded microcontroller platforms;ULCL - an ultra-lightweight cryptographic library for embedded systems;SPD-driven smart transmission layer based on a software defined radio test bed architecture;a meta-heuristically optimized fuzzy approach towards multi-metric security risk assessment in heterogeneous system of systems;towards embedded robot vision for multi-scale object recognition repeatability of interest points detected in half-octave binomial pyramids;embedded systems security challenges;a mobile multi agent system for routing in adhoc network;and an automatic vision system using optical scanning mechanism with near-infrared optics for solar cell wafer.

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Proceedings of the 4th Annual international conference on Computer Games, multimedia and Allied Technology, CGAT 2011 and 2nd Annual international conference on Cloud computing and Virtualization, CCV 2011

Proceedings of the 4th Annual International Conference on Co...

引用

4th Annual international conference on Computer Games, multimedia and Allied Technology, CGAT 2011 and 2nd Annual international conference on Cloud computing and Virtualization, CCV 2011

ISBN: (纸本)9789810886448

the proceedings contain 20 papers. the topics discussed include: teaching creativity and game design: postmortem on a game design and testing class;optimized strategies for the mastermind game;sustainable energy powered wireless game controller using electromagnetic induction;a novel method of teaching 3D game development to fresher students;a new AR user interface suitable for design space planning in the festival;an efficient peer selection algorithm for fast channel switching on Internet TV;a fast channel switching system for IPTV based on multichannel preview;audience perception of computer generated human facial behaviour;immersive technologies and personalised learning - the influence of games related technologies on 21st century learning;impact of flow on exploratory learning behavior in gaming and learning achievement;classifying agreement and disagreement level in online conversation;and games technology: console architectures, game engines and invisible interaction.

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Ubi-Media 2017 - Proceedings of the 10th international conference on Ubi-Media computing and Workshops with the 4th international Workshop on Advanced E-Learning and the 1st international Workshop on multimedia and IoT: Networks, Systems and Applications

Ubi-Media 2017 - Proceedings of the 10th International Confe...

引用

10th international conference on Ubi-Media computing and Workshops, Ubi-Media 2017

ISBN: (纸本)9781538627617

the proceedings contain 80 papers. the topics discussed include: low power HD video fast motion estimation algorithm based on signatures;novel QoS guaranteed cell selection schemes in LTE-A heterogeneous networks;real-time intrusion - detecting and alert system by image processing techniques;a mutual trust scheme for vehicular ad-hoc networks under sparse RSUS environment;budget and procurement analytics using open government data in thailand;a distributed SHVC video transcoding system;and create a puppet play and interative digital models with leap motion.

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Accelerating Traditional Object Detection Methods on Sophisticated embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophis...

引用

4th international conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, thierry Chouireb, Fatima Department of Physics Laboratory of Applied Sciences and Didactics École Normale Supérieure de Laghouat Laghouat Algeria Department of Electronics Telecommunication Signals and Systems Laboratory University Amar Telidji of Laghouat Laghouat Algeria Department of Computer Science Lab. Mathématiques Image et Applications Université de La Rochelle La Rochelle France

ISBN: (纸本)9783031821523

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. therefore, special hardware that can acquire, control, or execute in parallel is required. these specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual Processing Units VPUs, Tensor Processing Units TPUs, Neural Processing Units NPUs or Graphics Processing Units GPUs. this work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. this single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4× faster than the CPU for the HOG method and 2× for the Haar Cascade Classifier method. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Redefining Platform Data Mining Paradigms with Sequential Quadratic Programming Considering Optimization Models

Redefining Platform Data Mining Paradigms with Sequential Qu...

引用

Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Xiong Wenting Guangdong University of Science and Technology Dongguan Guangdong China

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Platform data mining is an important branch of data analysis. Traditional methods such as clustering have achieved satisfactory performance. To overcome the shortcomings of traditional algorithms in mathematical optimization, this study proposes a novel model based on the sequential quadratic programming algorithm. At the algorithm level, this study first analyses the characteristics of cloud platform data and its requirements for mining efficiency. At the same time, to solve these problems, this study proposed a new data analysis framework that combines sparse factor analysis and embedded database subspace detection. the designed model optimizes attribute dimension selection and dense region extraction to the greatest extent through distribution analysis and feature correlation evaluation. At the same time, the Bayesian network node expansion algorithm is used to model the association of discrete data, and then a cascade data generation method is designed based on this model. Finally, the Bayesian network parameters are optimized by the sequential quadratic programming algorithm, and the approximate value of the Hessian matrix is efficiently solved by the BFGS algorithm, thereby improving the accuracy of the data mining algorithm. the experimental part uses the cloud platform data-set as the target data-set and verifies the stability of the proposed algorithm.

关键词： Cloud computing Heuristic algorithms Computational modeling Clustering algorithms Approximation algorithms Feature extraction Data models Data mining Quadratic programming Optimization

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