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2nd International Conference on Electronic Information technology and Computer Engineering, EITCE 2018

作者： Zhuang, Yifan Wang, Hongwei Chen, Xi School of Automation Huazhong University of Science and Technology Wuhan430074 China School of Management Huazhong University of Science and Technology Wuhan430074 China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Wuhan430074 China

The BDI model has always been the focus of subject modeling research, which includes three kinds of thinking states of the rational subject: Belief, Desire and Intention. Belief is the cognition of agent to the world;it is a collection of environmental information, other agent information, and its own information that the agent has;and it is also the basis of the agent's thinking activity. Due to differences in the individual's living environment and experience, the formation of heterogeneous beliefs is an important issue in the BDI model study. This article divides individual belief set into two parts: knowledge belief and achievable belief. This article proposes an overall framework for the formation of individual heterogeneity beliefs: First, the individual's knowledge experience is modeled, and the empirical knowledge is structured and quantified into binary propositions;then the BP neural network learn and memory propositions of different combinations to form heterogeneous beliefs. Experiments show that this method can simulate the heterogeneity of individual beliefs caused by the individual's own experience, and can realize the belief generation mechanism of gradual information flow, limited attention and heterogeneous priors. © The Authors, published by EDP Sciences, 2018.

关键词： Neural networks

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Transferable Contrastive Network for Generalized Zero-Shot Learning

Transferable Contrastive Network for Generalized Zero-Shot L...

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International Conference on Computer Vision (ICCV)

作者： Huajie Jiang Ruiping Wang 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 Shanghai Institute of Microsystem and Information Technology CAS Shanghai China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, most existing approaches are easy to overfit the sources classes in generalized zero-shot learning (GZSL) task, which indicates that they learn little knowledge about target classes. To tackle such problem, we propose a novel Transferable Contrastive Network (TCN) that explicitly transfers knowledge from the source classes to the target classes. It automatically contrasts one image with different classes to judge whether they are consistent or not. By exploiting the class similarities to make knowledge transfer from source images to similar target classes, our approach is more robust to recognize the target images. Experiments on five benchmark datasets show the superiority of our approach for GZSL.

关键词： Semantics Visualization Image recognition Robustness Target recognition Task analysis Fuses

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Preprocessing method of night vision image application in apple harvesting robot

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International Journal of Agricultural and Biological Engineering 2018年第2期11卷 158-163页

作者： Weikuan Jia Yuanjie Zheng De’an Zhao Xiang Yin Xiaoyang Liu Ruicheng Du School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Key Laboratory of Facility Agriculture Measurement and Control Technology and Equipment of Machinery Industry Jiangsu UniversityZhenjiang 212013China School of Agricultural Engineering and Food Science Shandong University of TechnologyZibo 255000China Institute of Life Sciences and Key Lab of Intelligent Information Processing Shandong Normal UniversityJinan 250014China

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for *** machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation *** to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting *** different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be *** addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise *** results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper *** preprocessing method can provide a theoretical and technical reference for subsequent image processing.

关键词： apple harvesting robot night vision image preprocessing method color analysis noise analysis

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A Hybrid Replacement Strategy for MOEA/D 13th

A Hybrid Replacement Strategy for MOEA/D

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Chen, Xiaoji Shi, Chuan Zhou, Aimin Xu, Siyong Wu, Bin Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University Shanghai200062 China

ISBN: (纸本)9789811328251

In MOEA/D, the replacement strategy plays a key role in balancing diversity and convergence. However, existing adaptive replacement strategies either focus on neighborhood or global replacement strategy, which may have no obvious effects on balance of diversity and convergence in tackling complicated MOPs. In order to overcome this shortcoming, we propose a hybrid mechanism balancing neighborhood and global replacement strategy. In this mechanism, a probability threshold is applied to determine whether to execute a neighborhood or global replacement strategy, which could balance diversity and convergence. Furthermore, we design an offspring generation method to generate the high-quality solution for each subproblem, which can ease mismatch between subproblems and solutions. Based on the classic MOEA/D, we design a new algorithm framework, called MOEA/D-HRS. Compared with other state-of-the-art MOEAs, experimental results show that the proposed algorithm obtains the best performance. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Constrained Multi-scale Dense Connections for Accurate Biomedical Image Segmentation

Constrained Multi-scale Dense Connections for Accurate Biome...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiawei Zhang Yanchun Zhang Shanfeng Zhu Xiaowei Xu Shanghai key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China College of Engineering and Science Victoria University Melbourne Australia ISTBI and Shanghai Institute of Artificial Intelligence Algorithms Fudan University Shanghai China Nanjing University Institute of Artificial Intelligence Biomedicine Nanjing China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China Guangdong Provincial People’s Hospital Guangdong Cardiovascular Institute Guangzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Biomedical image segmentation plays a critical role in clinical diagnosis and medical intervention. Recently, a variety of deep neural networks have boosted the biomedical image segmentation performance with a large margin, which adopts dense connections to explore rich representations in multiple scales. In multi-scale dense connections, features from all or most scales are fused or iteratively aggregated. In this paper, we propose constrained multi-scale dense connections (CMDC) for accurate biomedical image segmentation, which only fuse features from the nearest scales containing the most relevant appearance or semantic information. Based on CMDC, we further construct constraint multi-scale dense networks (CMD-Net) by applying CMDC to existing segmentation networks. Experiments across various architectures (including FCN-8s, U-Net, and DeepLabV3) and datasets (including GlaS, CRAG, KID, and ECS) demonstrate that CMD-Net not only outperforms existing schemes on both accuracy and efficiency but also can be easily generalized to a variety of segmentation networks. In addition, CMD-Net achieves state-of-the-art performance on two instance segmentation datasets, GlaS and CRAG.

关键词： Decoding Image segmentation Image resolution Biomedical imaging Glands Fuses Cancer

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Gaussian curvature filter on 3d mesh

arXiv

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arXiv 2020年

作者： Tang, Wenming Gong, Yuanhao Liu, Kanglin Liu, Jun Pan, Wei Liu, Bozhi Qiu, Guoping College of Information Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Mechanical & Automotive Engineering South China University of Technology Department of Research and Development OPT Machine Vision Tech Co. Ltd Jinsheng Road Changan Dongguan Guangdong523860 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Minimizing Gaussian curvature of meshes is fundamentally important for obtaining smooth and developable surfaces. However, there is a lack of computationally efficient and robust Gaussian curvature optimization method. In this paper, we present a simple yet effective method that can efficiently reduce Gaussian curvature for 3D meshes. We first present the mathematical foundation of our method, which states that for any point on a developable surface there must be another point that lives on its tangent plane. Then, based on this theoretical insight, we introduce a simple and robust implicit Gaussian curvature optimization method named Gaussian Curvature Filter (GCF). GCF implicitly minimizes Gaussian curvature without the need to explicitly calculate the Gaussian curvature itself. GCF is highly efficient and is 20 times faster than the classical standard Gaussian curvature flow method. We present extensive experiments to demonstrate that GCF significantly outperforms state-of-the-art methods in minimizing Gaussian curvature, geometric feature preserving smoothing and mesh noise removal. This method can be used in a large range of applications that involve Gaussian curvature. Copyright © 2020, The Authors. All rights reserved.

关键词： Mesh generation

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Fcsr-gan: Joint face completion and super-resolution via multi-task learning

arXiv

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arXiv 2019年

作者： Cai, Jiancheng Han, Hu Shan, Shiguang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University ofChinese Academy of Sciences Beijing100049 China Institute of Computing Technology CAS Beijing100190 China Peng Cheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing100049 China

Combined variations containing low-resolution and occlusion often present in face images in the wild, e.g., under the scenario of video surveillance. While most of the existing face image recovery approaches can handle only one type of variation per model, in this work, we propose a deep generative adversarial network (FCSR-GAN) for performing joint face completion and face super-resolution via multi-task learning. The generator of FCSR-GAN aims to recover a high-resolution face image without occlusion given an input low-resolution face image with occlusion. The discriminator of FCSR-GAN uses a set of carefully designed losses (an adversarial loss, a perceptual loss, a pixel loss, a smooth loss, a style loss, and a face prior loss) to assure the high quality of the recovered high-resolution face images without occlusion. The whole network of FCSR-GAN can be trained end-to-end using our two-stage training strategy. Experimental results on the public-domain CelebA and Helen databases show that the proposed approach outperforms the state-of-the-art methods in jointly performing face super-resolution (up to 8 ×) and face completion, and shows good generalization ability in cross-database testing. Our FCSR-GAN is also useful for improving face identification performance when there are low-resolution and occlusion in face images. The code of FCSR-GAN is available at: https://***/swordcheng/FCSR-GAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Discovering Classification Dimensions for Managing Scientific Resources

Discovering Classification Dimensions for Managing Scientifi...

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Bing Ma Hai Zhuge Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS University of Chinese Academy of Sciences CAS Beijing China Aston University Birmingham UK KLIIP ICT University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728158235

ISBN: (纸本)9781728158242

The Resource Space Model is a systematic theory and method for managing the contents of resources by a multi-dimensional classification space. Different dimensions represent different classification methods. Each dimension can be regarded as a classification tree. Design of Resource Space is mainly based on domain knowledge and experience of designer. This paper implements an approach to automatic discovering dimensions from texts (taking scientific papers as experimental data). It consists of four stages: 1) representing each text as a collection of tuples in the form of (word, value); 2) constructing an initial dimension; 3) constructing the hierarchical dimensions based on the initial dimension; and 4) post-processing of outlier texts. The experimental results show that our model achieves a significant improvement of performance on classifying texts.

关键词： Text categorization Training Clustering algorithms Protocols Training data Partitioning algorithms Semantics

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An Early Forest Fire Detection Method Based on Unmanned Aerial Vehicle Vision

An Early Forest Fire Detection Method Based on Unmanned Aeri...

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第30届中国控制与决策会议

作者： Sha Ma Youmin Zhang Jing Xin Yingmin Yi Ding Liu Han Liu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Traditional forest fire detection methods based on flame are subject to detection delays due to the later appearrance of flame than smoke. When the flame is detected, the fire might have already been spread and out of ***, the objective of this paper is to study new fire detection method based on smoke. First a quadrotor unmanned aerial vehicle(UAV) is adopted to capture the fire video;then the collected video sequence will be processed by a series of preprocessing including filtering and image enhancement;finally a new significance detection algorithm is investigated for early and efficient smoke detection. Experimental results show that the algorithm can detect smoke effectively.

关键词： UAV vision Fire smoke Fire detection Significance detection algorithm

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Towards ultra-reliable low-latency communications: Typical scenarios, possible solutions, and open issues

arXiv

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arXiv 2019年

作者： Feng, Daquan She, Changyang Ying, Kai Lai, Lifeng Hou, Zhanwei Quek, Tony Q.S. Li, Yonghui Vucetic, Branka Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China School of Electrical and Information Engineering University of Sydney Sydney Australia Sharp Laboratories of America CamasWA United States ISTD Pillar Singapore University of Technology and Design Singapore

Ultra-reliable low-latency communications (URLLC) has been considered as one of the three new application scenarios in the 5th Generation (5G) New Radio (NR), where the physical layer design aspects have been specified. With the 5G NR, we can guarantee the reliability and latency in radio access networks. However, for communication scenarios where the transmission involves both radio access and wide area core networks, the delay in radio access networks only contributes to part of the end-to-end (E2E) delay. In this paper, we outline the delay components and packet loss probabilities in typical communication scenarios of URLLC, and formulate the constraints on E2E delay and overall packet loss probability. Then, we summarize possible solutions in the physical layer, the link layer, the network layer, and the cross-layer design, respectively. Finally, we discuss the open issues in prediction and communication co-design for URLLC in wide area large scale networks. Copyright © 2019, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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