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7th International Conference on Rough Sets and Knowledge Technology, RSKT 2012

作者： Yao, Dengbao Wang, Cuicui Mao, Junjun Zhang, Yanping School of Mathematical Sciences Anhui University Hefei 230039 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China Department of Computer Science University of Houston Houston TX 77204-3010 United States

ISBN: (纸本)9783642318993

Fuzzy soft set (FSs) is a novel mathematical tool, in order to describe and measure uncertain information of FSs perfectly, granularity analysis based on covering about FSs is originally discussed in this paper. Firstly, the α-dominance relation between any two objects is built by constructing the possibility degree or the weighted possibility degree after standardization, thus α-dominance class and α-covering approximation space could be generated on this relation. Secondly, granular capacity is proposed to measure the granular information of variable precision through introducing concepts of the description set and the indistinguishability set, Finally, an illustrative example is analysed to reflect dynamic changes of uncertain information under different granular structure. © 2012 Springer-Verlag.

关键词： Set theory

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Vision-Based Fruit Recognition Via Multi-Scale Attention Cnn

SSRN

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SSRN 2022年

作者： Min, Weiqing Wang, Zhiling Yang, Jiahao Liu, Chunlin Jiang, Shuqiang The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Fruit quality assessment, grading and sorting are of vital importance to fruit processing, and all these involve fruit recognition. Vision-based fruit recognition can recognize fruit automatically and further support more applications such as fruit-picking robots, self-checkout service, and dietary guidance. Recent works resort to Convolutional Neural Networks (CNNs) for vision-based fruit recognition because of their powerful expressive capacity. However, most works simply utilize existing CNNs and ignore characteristics of fruit images, resulting in sub-optimal recognition performance. To solve this problem, we adopt a Multi-Scale Attention Network (MSANet), which explores attention from different layers of CNNs and aggregates various visual attentional features from different levels into final comprehensive representations. Extensive evaluations on four fruit benchmark datasets demonstrate that the method achieves state-of-the-art recognition performance. In addition, we report various experimental results from different deep networks as baselines. Codes and models will be released upon publication. Models and code are available at http://123.57.42.89/codes/***. © 2022, The Authors. All rights reserved.

关键词： Computer vision

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A Comprehensive Assessment Method of Asset Importance for key Asset Protection 2019

A Comprehensive Assessment Method of Asset Importance for Ke...

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Proceedings of the 2019 the 9th International Conference on Communication and Network Security

作者： Xiaoling Gao Yanhua Liu Yanbin Qiu Huajun Liu College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781450376624

In a given system network, an important prerequisite for security risk control is how to accurately calculate the impact of different host assets in the topology on the spread of attack risk. In this regard, we propose a comprehensive asset importance assessment model for critical asset protection. First, the importance of local static assets in the topology was evaluated using three methods: degree centrality, mediation centrality, and closeness centrality. At the same time, PageRank was used to assess the importance of local dynamic assets for asset level changes. By combining the above two methods, and using K-CORE to evaluate the core position of the host in the network topology as the global weight indicator of the importance of assets. The comprehensive evaluation results of the importance of topology-oriented assets was finally obtained, and the validity of the model was verified through the experiment of the simulation ER random network.

关键词： Centrality PageRank Asset importance key asset protection K-CORE

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TEMPORALLY MULTIPLE DYNAMIC TEXTURES SYNTHESIS USING PIECEWISE LINEAR DYNAMIC SYSTEMS

TEMPORALLY MULTIPLE DYNAMIC TEXTURES SYNTHESIS USING PIECEWI...

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IEEE International Conference on Image processing

作者： Xing Yan Hong Chang Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China

ISBN: (纸本)9781479923427

Real-world nonlinear dynamic textures (DTs) usually consist of temporally multiple linear DTs which cannot be correctly modeled by previous works. In this paper, we propose piecewise linear dynamic systems (PLDS) to model temporally multiple DTs. PLDS simultaneously decides the temporal segmentation, models each DT segment with an LDS and the whole DT by switching between the LDS'. Experimental results verify that PLDS can capture the stochastic and dynamic nature of temporally multiple DTs and it synthesizes nonlinear DTs without decay or divergence. An EM-like algorithm iterating between sequence division and LDS' fitting is adopted to learn the model parameters.

关键词： Temporally Multiple Dynamic Textures Piecewise Linear Dynamic Systems Temporal Segmentation Local Digital Switch model parameters Programmable logic devices textures Stochastic dynamic decay Decay biodeterioration synthesis

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PAS-Net: Pose-based and Appearance-based Spatiotemporal Networks Fusion for Action Recognition

PAS-Net: Pose-based and Appearance-based Spatiotemporal Netw...

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International Conference on Automatic Face and Gesture Recognition

作者： Changzhen Li Jie Zhang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

Human poses play important roles in action analysis. However, most state-of-the-art approaches in action recognition ignore the importance of human poses and rarely leverage the pose information for further improving the recognition performance. In this paper, we propose a novel network architecture, which simultaneously considers the appearance information and pose knowledge for robust action recognition. We explore various architectures for fusing the appearance and pose information rather than simply averaging scores at the final layer. Moreover, a novel training strategy is proposed to reduce the influence of overfitting for limited training data. Extensive experiments show that our method achieves competitive performance on the popular benchmarks, i.e., UCF-101 and HMDB-51.

关键词： Feature extraction Three-dimensional displays Spatiotemporal phenomena Computer architecture Two dimensional displays Fuses Training

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An efficient two-stage level set segmentation framework for overlapping plant leaf image

An efficient two-stage level set segmentation framework for ...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Wang, Xiao-Feng Min, Hai Key Lab. of Network and Intelligent Information Processing Department of Computer Science and Technology Hefei University Hefei Anhui 230022 China Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230031 China Department of Automation University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9783642315879

In this paper, an efficient two-stage segmentation framework was proposed to address the plant leaf image with overlapping phenomenon, which is built based on the leaf approximate symmetry and level set evolution theory. In the pre-segmentation stage, a straight line was manually set on the target leaf to approximate the principal leaf vein and the Local Chan-Vese (LCV) model was used on the global image region to help searching the so-called un-overlapping contour in target leaf. In the formal segmentation stage, the symmetry detection was performed based on the pre-defined approximated principal vein to obtain the narrow-band evolution region and the second initial contour. Next, the LCV model was once again used to find the complete target leaf contour in the narrow-band evolution region. Finally, experiments on some real leaf images with overlapping phenomenon have demonstrated the efficiency and robustness of the proposed segmentation framework. © 2012 Springer-Verlag.

关键词： Image segmentation

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Algorithms of Integrated Student Modeling in Online Virtual Educational Community

Algorithms of Integrated Student Modeling in Online Virtual ...

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2001 International Conferences on Info-tech and Info-net

作者： Yonghong Tian Tiejun Huang Wen Gao Digital Media Lab Institute of Computing TechnologyChinese Academy of Sciences Laboratory of Intelligent Information Processing Chinese Academy of Sciences

The paper focuses on how to construct student models in the online virtual educational *** firstly analyze social interaction between students in the community,and present some algorithms to model students'(personal and shared) needs, preferences or knowledge structures in interaction ***,the paper proposes an integrated student model combined with student modeling in information services and task processes,and emphasizes that it builds up a foundation to personalize information services and customize online educational programs.

关键词： Virtual community interaction activities integrated student modeling personalized study assistants

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OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment

arXiv

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arXiv 2023年

作者： Alfasly, Saghir Al-Huda, Zaid Bello, Saifullah Elazab, Ahmed Lu, Jian Xu, Chen Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China School of Computing and Artificial Intelligent Southwest Jiaotong University China School of Biomedical Engineering Shenzhen University China

Current deep models provide remarkable object detection in terms of object classification and localization. However, estimating object rotation with respect to other visual objects in the visual context of an input image still lacks deep studies due to the unavailability of object datasets with rotation annotations. This paper tackles these two challenges to solve the rotation estimation of a parked bike with respect to its parking area. First, we leverage the power of 3D graphics to build a camera-agnostic well-annotated Synthetic Bike Rotation Dataset (SynthBRSet). Then, we propose an object-to-spot rotation estimator (OSRE) by extending the object detection task to further regress the bike rotations in two axes. Since our model is purely trained on synthetic data, we adopt image smoothing techniques when deploying it on real-world images. The proposed OSRE is evaluated on synthetic and real-world data providing promising results. Our data and code are available at https://***/OSRE/. © 2023, CC BY.

关键词： Rotation

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A Biased Random key Heuristic Genetic Approach for the Payload Packing Design of A Satellite Module

A Biased Random Key Heuristic Genetic Approach for the Paylo...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ziqiang Li Wenqian Ruan Yu Kang School of Computer Science Xiangtan University Xiangtan Hunan China Ministry Education Key Laboratory of Intelligent Computing & Information Processing Xiangtan University Xiangtan Hunan China

ISBN: (纸本)9781728165981

Existing heuristic evolutionary approaches are effective to solve the multi-module satellite packing problem. But for them, payload allocation and packing optimization are achieved in two different stages. And for their heuristics there are faults of weaker generality and less candidate location. So, a biased random key heuristic genetic approach is proposed for the collaborative payload allocation and packing for multi-module satellite (MSRKHGA). The firstly, by coding and decoding the random key individual and using GA operator, the organic combination of the payload allocation and packing optimization is achieved. Secondly, by region-positioning it can avoid the time-consuming overlapping calculation between individuals and dynamically generate more candidate regions for payloads to be packed. Numerical experiments show that all index of the proposed MSRKHGA approach are superior obviously to those of existing approaches.

关键词： Satellites Heuristic algorithms Genetics Encoding Decoding Resource management Optimization

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Pose-adaptive hierarchical attention network for facial expression recognition

arXiv

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arXiv 2019年

作者： Liu, Yuanyuan Peng, Jiyao Zeng, Jiabei Shan, Shiguang School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Center for Excellence in Brain Science and Intelligence Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Multi-view facial expression recognition (FER) is a challenging task because the appearance of an expression varies in poses. To alleviate the influences of poses, recent methods either perform pose normalization or learn separate FER classifiers for each pose. However, these methods usually have two stages and rely on good performance of pose estimators. Different from existing methods, we propose a pose-adaptive hierarchical attention network (PhaNet) that can jointly recognize the facial expressions and poses in unconstrained environment. Specifically, PhaNet discovers the most relevant regions to the facial expression by an attention mechanism in hierarchical scales, and the most informative scales are then selected to learn the poseinvariant and expression-discriminative representations. PhaNet is end-to-end trainable by minimizing the hierarchical attention losses, the FER loss and pose loss with dynamically learned loss weights. We validate the effectiveness of the proposed PhaNet on three multi-view datasets (BU-3DFE, Multi-pie, and KDEF) and two in-the-wild FER datasets (AffectNet and SFEW). Extensive experiments demonstrate that our framework outperforms the state-of-the-arts under both within-dataset and cross-dataset settings, achieving the average accuracies of 84.92%, 93.53%, 88.5%, 54.82% and 31.25% respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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