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4th International Conference on Materials Science and Information Technology, MSIT 2014

作者： Huang, Yi Ma, Xin Qiang Liu, You Yuan Li, Dan Ning Key Laboratory of Machine Vision and Intelligent Information System Chongqing University of Arts and Sciences Chongqing 402160 China Guizhou Academy of Science Guiyang 550001 China School of Computer Science and Technology Guizhou University Guiyang 550025 China

ISBN: (纸本)9783038351733

Cloud computing represents one of the most significant shifts in information technology many of us are likely to see in our lifetimes. It offers an innovative business model for organizations to adopt IT services without upfront investment. Cloud computing moves the application software and databases to large data centers, where the management of the data and services may not be fully trustworthy. Security is one of the major issues which hamper the growth of cloud. The security for cloud computing is emerging area for study and this paper provide technical components of cloud computing and security idea in terms of cloud computing based on analysis of cloud security treats. © (2014) Trans Tech Publications, Switzerland.

关键词： Access control

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Light field imaging for computer vision:a survey

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Frontiers of Information Technology & Electronic Engineering 2022年第7期23卷 1077-1097页

作者： Chen JIA Fan SHI Meng ZHAO Shengyong CHEN Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China

Light field(LF)imaging has attracted attention because of its ability to solve computer vision *** this paper we briefly review the research progress in computer vision in recent *** most factors that affect computer vision development,the richness and accuracy of visual information acquisition are *** imaging technology has made great contributions to computer vision because it uses cameras or microlens arrays to record the position and direction information of light rays,acquiring complete three-dimensional(3D)scene *** imaging technology improves the accuracy of depth estimation,image segmentation,blending,fusion,and 3D *** has also been innovatively applied to iris and face recognition,identification of materials and fake pedestrians,acquisition of epipolar plane images,shape recovery,and LF ***,we further summarize the existing problems and the development trends of LF imaging in computer vision,including the establishment and evaluation of the LF dataset,applications under high dynamic range(HDR)conditions,LF image enhancement,virtual reality,3D display,and 3D movies,military optical camouflage technology,image recognition at micro-scale,image processing method based on HDR,and the optimal relationship between spatial resolution and four-dimensional(4D)LF information *** imaging has achieved great success in various *** the past 25 years,more than 180 publications have reported the capability of LF imaging in solving computer vision *** summarize these reports to make it easier for researchers to search the detailed methods for specific solutions.

关键词： Light field imaging Camera array Microlens array Epipolar plane image computer vision

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Brain tumor cell density estimation from multi-modal MR images based on a synthetic tumor growth model

Brain tumor cell density estimation from multi-modal MR imag...

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15th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Geremia, Ezequiel Menze, Bjoern H. Prastawa, Marcel Weber, M.-A. Criminisi, Antonio Ayache, Nicholas Asclepios Research Project INRIA Sophia-Antipolis France Computer Science and Artificial Intelligence Laboratory MIT United States Computer Vision Laboratory ETH Zurich Switzerland Scientific Computing and Imaging Institute University of Utah United States Diagnostic and Interventional Radiology Heidelberg University Hospital Germany Machine Learning and Perception Group Microsoft Research Cambridge United Kingdom

ISBN: (纸本)9783642366192

This paper proposes to employ a detailed tumor growth model to synthesize labelled images which can then be used to train an efficient data-driven machine learning tumor predictor. Our MR image synthesis step generates images with both healthy tissues as well as various tumoral tissue types. Subsequently, a discriminative algorithm based on random regression forests is trained on the simulated ground truth to predict the continuous latent tumor cell density, and the discrete tissue class associated with each voxel. The presented method makes use of a large synthetic dataset of 740 simulated cases for training and evaluation. A quantitative evaluation on 14 real clinical cases diagnosed with low-grade gliomas demonstrates tissue class accuracy comparable with state of the art, with added benefit in terms of computational efficiency and the ability to estimate tumor cell density as a latent variable underlying the multimodal image observations. The idea of synthesizing training data to train data-driven learning algorithms can be extended to other applications where expert annotation is lacking or expensive. © 2013 Springer-Verlag.

关键词： Tumors

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Layout identification of printed mathematical formula for recognition

Layout identification of printed mathematical formula for re...

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International Conference on Information Engineering and computer Science

作者： Tian, Xue-Dong Zhao, Yan Wang, Hui Wang, Qiang-Jun College of Mathematics and Computer Science Hebei University Baoding China Hebei Key Laboratory of Machine Learning and Computational Intelligence Baoding China Baoding Senior Technical College Baoding China College of Literature Hebei University Baoding China

ISBN: (纸本)9781424479412

Printed mathematical formulas edited by different soft wares have some obvious differences. To distinguish it before recognition is beneficial to the formula recognition. Based on the statistical analysis to the characteristics of Latex and Word typesetting, an algorithm is designed to identify Latex and Word layout of printed formulas. The experiment indicates that this algorithm can achieve a relative high accuracy to standard printed documents. ©2010 IEEE.

关键词： Latexes

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Identifying driver genes in cancer based on Pareto optimality consensus

Identifying driver genes in cancer based on Pareto optimalit...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Deng, Zheng Wu, Jingli Chen, Xiaorong Li, Gaoshi Guangxi Normal University College of Computer Science Information Technology Guilin China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Wuzhou University Guangxi Key Laboratory of Machine Vision Intelligent Control Wuzhou China

ISBN: (纸本)9781665468190

An important issue in cancer genomics is the identification of driver genes. It is significant for the discovery of key biomarkers and the development of effective personalized therapies. In this paper, a computated method PGScore is proposed. It scores genes at multilayer and integrates the scores to identify cancer driver genes based on Pareto Optimality Consensus(POC) strategy. PGScore uses random walks to reevaluate gene mutations, and integrates differential expression of mRNA and miRNA in normal and cancer samples. It measures the centrality of the gene in the network according to the weight of its direct and indirect neighbors, and finally integrates the above layers to get the final priority of the genes. We compare PGScore with state-of-the-art cancer driver genes prioritization methods on two real cancer datasets. The results show that PGScore can obtain better performance in identification accuracy and the partial area under the ROC(pAUC) curve on multiple reference databases. © 2022 IEEE.

关键词： Gene expression

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ActMAD: Activation Matching to Align Distributions for Test-Time-Training

arXiv

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arXiv 2022年

作者： Mirza, Muhammad Jehanzeb Soneira, Pol Jané Lin, Wei Kozinski, Mateusz Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision TU Graz Austria Christian Doppler Laboratory for Embedded Machine Learning Institute of Control Systems KIT Germany Christian Doppler Laboratory for Semantic 3D Computer Vision

Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contrast to existing methods, which model the distribution of entire channels in the ultimate layer of the feature extractor, we model the distribution of each feature in multiple layers across the network. This results in a more fine-grained supervision and makes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C. ActMAD is also architecture- and task-agnostic, which lets us go beyond image classification, and score 15.4% improvement over previous approaches when evaluating a KITTI-trained object detector on KITTI-Fog. Our experiments highlight that ActMAD can be applied to online adaptation in realistic scenarios, requiring little data to attain its full performance. © 2022, CC BY.

关键词： Chemical activation

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ActMAD: Activation Matching to Align Distributions for Test-Time-Training

ActMAD: Activation Matching to Align Distributions for Test-...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M. Jehanzeb Mirza Pol Jané Soneira Wei Lin Mateusz Kozinski Horst Possegger Horst Bischof Institute for Computer Graphics and Vision TU Graz Austria Christian Doppler Laboratory for Embedded Machine Learning Institute of Control Systems KIT Germany Christian Doppler Laboratory for Semantic 3D Computer Vision

Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform this adaptation via Activation Matching (ActMAD): We analyze activations of the model and align activation statistics of the OOD test data to those of the training data. In contrast to existing methods, which model the distribution of entire channels in the ultimate layer of the feature extractor, we model the distribution of each feature in multiple layers across the network. This results in a more fine-grained supervision and makes ActMAD attain state of the art performance on CIFAR-100C and Imagenet-C. ActMAD is also architecture-and task-agnostic, which lets us go beyond image classification, and score 15.4% improvement over previous approaches when evaluating a KITTI-trained object detector on KITTI-Fog. Our experiments highlight that ActMAD can be applied to online adaptation in realistic scenarios, requiring little data to attain its full performance.

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Collaborative visual navigation

arXiv

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arXiv 2021年

作者： Wang, Haiyang Wang, Wenguan Zhu, Xizhou Dai, Jifeng Wang, Liwei Key Laboratory of Machine Perception MOE Peking University SenseTime Research Computer Vision Lab ETH Zurich

As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques. However, previous MARL methods largely focused on grid-world like or game environments;MAS in visually-rich environments has remained less explored. To narrow this gap and emphasize the crucial role of perception in MAS, we propose a large-scale 3D dataset, CollaVN, for multi-agent visual navigation (MAVN). In CollaVN, multiple agents are entailed to cooperatively navigate across photo-realistic environments to reach target locations. Diverse MAVN variants are explored to make our problem more general. Moreover, a memory-augmented communication framework is proposed. Each agent is equipped with a private, external memory to persistently store communication information. This allows agents to make better use of their past communication information, enabling more efficient collaboration and robust long-term planning. In our experiments, several baselines and evaluation metrics are designed. We also empirically verify the efficacy of our proposed MARL approach across different MAVN task settings. © 2021, CC BY.

关键词： Multi agent systems

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An improved surround suppression model based on orientation contrast for boundary detection

An improved surround suppression model based on orientation ...

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International Conference on Pattern Recognition

作者： Hui Zhang Bojun Xie Jian Yu Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Hebei China Laboratory of Machine Learning and Cognitive Computation School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9781467322164

This paper proposes an unsupervised bottom-up boundary detection algorithm, which is an improved surround suppression model based on orientation contrast. First, the candidate boundary set is obtained by the edge focusing algorithm. Second, the orientation contrast map is constructed using the response of Gabor filter. The suppression term is computed on orientation contrast map using steerable filter, which can effectively differentiate step edge from texture edge. Using low-level image features, the boundary map can be used as preprocessing step for image segmentation and/or object detection. The detection approach has been validated on Rug dataset and the average of figure of merit shows an improvement of 15%.

关键词： Image edge detection Focusing Detection algorithms Detectors Image segmentation Educational institutions computer vision

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BPVFL: A Bidirectional Privacy-Preserving Verifiable Federated learning Framework with Homomorphic Encryption

BPVFL: A Bidirectional Privacy-Preserving Verifiable Federat...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Jingwei Chen, Sijing Zhu, Junrong Sun, Rong Du, Xiaojiang Guizani, Mohsen Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Isn Xi'an710071 China Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken United States Department of Machine Learning Abu Dhabi999041 United Arab Emirates

ISBN: (纸本)9798350351255

Federated learning, while advancing data privacy, faces risks of sensitive information leakage through parameter updates, making it susceptible to inference and data reconstruction attacks. Fraudulent behaviors by central servers or clients can undermine the integrity of model training, thereby reducing accuracy and affecting decision-making quality. This paper introduces a bidirectional, privacy-preserving verifiable federated learning framework(BPVFL) built on homomorphic encryption and a novel three-party zero-knowledge protocol. This framework guarantees the integrity of server aggregation and the credibility of the information uploaded by clients. Experimental results demonstrate that BPVFL effectively protects client privacy, prevents fraud by servers and certain clients, and efficiently handles numerous client disconnections with minimal overhead. © 2024 IEEE.

关键词： Federated learning

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