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International Conference on Multimedia and Signal processing

作者： Jin, Biao Hu, Wenlong Zhang, Yu CAS Graduate School Key Laboratory of Technology In Geo-spatial Information Processing and Application System Institute of Electronic Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronic Chinese Academy of Sciences CAS Beijing China China Electric Power Research Institute Beijing China

ISBN: (纸本)9780769543567

Multi-object tracking is important in many computer vision applications. The major difficulties are due to inter-object or scene occlusion and data association. In this paper, we present a method to automatically detect and track multiple objects in complicated scenario from a single camera that is stationary. The proposed detecting method can prevent the moving objects updated to the background and can restrain ghosts. The moving objects are modeled fusing the hue and edge features. The system achieves tracking the object using the proposed associated strategy based on multilevel track queues. Experiments show the effectiveness of our approach under different conditions with complete occlusions. © 2011 IEEE.

关键词： Object detection

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A Hybrid MAC Protocol for Data Transmission in Smart Grid 33

A Hybrid MAC Protocol for Data Transmission in Smart Grid

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第三十三届中国控制会议

作者： WANG Fang KANG Yu TAN Xiaobin YU Kai Department of Automation and CAS Key Laboratory of Technology in Geo-spatial Information processing and application SystemUniversity of Science and Technology of China Shanghai Institute of Micro-system and Information Technology Chinese Academy of Sciences

ISBN: (纸本)9781479947249

In the information transmission process of Smart Grid, the collection and transmission of power consumption data of the massive users are very important. The data can be classified into the periodic and the alarm data. Different types of data are different in terms of their priority, reliability and real-time, and the alarm data has more rigid requirements of priority, real-time and reliability. Based on the characteristics of the data transmitted by smart grid and the QoS requirement, a new hybrid MAC protocol, HTC-MAC(Hybrid TDMA/CSMA MAC) protocol is proposed. Based on TDMA /CSMA and their combination, this protocol proposes an alarm priority function(APF) and a node clustering method to allocates the resources dynamically, in order to reduce the transmission delay and data loss. Simulation results show that the protocol has a good performance in reducing packet loss rate and network communication delay.

关键词： Smart grid MAC protocol CDMA TDMA

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Improved S2ANet based on attention mechanism for small target detection in remote sensing images

Improved S2ANet based on attention mechanism for small targe...

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2021 CIE International Conference on Radar, Radar 2021

作者： Lu, Dongdong Tie, Wenjie Lei, Songlin Qiu, Xiaolan Suzhou Aerospace Information Research Institute Laboratory of Spatial Information Intelligent Processing System Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Beijing China

ISBN: (纸本)9781665498142

Deep learning has achieved remarkable results in the field of target detection and recognition. For small targets in images, image pyramid can be used to fuse multi-scale features to improve detection performance. However, when test on remote sensing images, it is found that some important small goals will still be missed. Aiming at the problems of small target detection in remote sensing images, this paper proposes two S2ANet improved network variants based on the attention mechanism for synthetic aperture radar(SAR) payload and optical payload: for SAR images, CBAM(Convolutional Block Attention Module) attention is added to the backbone network to increase the attention of the feature extraction network for small targets in the channel and space dimensions;For optical images, firstly increase the CBAM channel attention and spatial attention in the backbone, secondly, the SE(Squeeze And Excitation) channel attention is added to the input features before the channel dimension fusion of the pyramid structure, then the attention-enhanced targets are fully integrated through up-sampling and horizontal splicing operations to improve the missed alarm rate and recall rate of small remote sensing targets. Finally, a comparative verification was carried out on the constructed airborne KU-band SAR data set and the optical remote sensing aircraft data set composed of some DOTA1.5 and FAIR1M data sets. The experimental results proved that the designed two variant networks can be used for small remote sensing. There have been certain improvements in the missed alarm rate, accuracy rate, and recall rate of the target. © 2021 IEEE.

关键词： Radar remote sensing Radar detection Object detection Radar imaging Optical imaging Feature extraction Adaptive optics

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Comparative deep learning of hybrid representations for image recommendations

Comparative deep learning of hybrid representations for imag...

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2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

作者： Lei, Chenyi Liu, Dong Li, Weiping Zha, Zheng-Jun Li, Houqiang CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781467388511

In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two subnetworks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other stateof-the-art image recommendation solutions.

关键词： Pattern recognition

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Sign language recognition with multi-modal features 17th

Sign language recognition with multi-modal features

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Pu, Junfu Zhou, Wengang Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9783319488950

We study the problem of recognizing sign language automatically using the RGB videos and skeleton coordinates captured by Kinect, which is of great significance in communication between the deaf and the hearing societies. In this paper, we propose a sign language recognition (SLR) system with data of two channels, including the gesture videos of the sign words and joint trajectories. In our framework, we extract two modals of features to represent the hand shape videos and hand trajectories for recognition. The variation of gesture is obtained by 3D CNN and the activations of fully connected layers are used as the representations of these sign videos. For trajectories, we use the shape context to describe each joint, and combine them all within a feature matrix. After that, a convolutional neural network is applied to generate a robust representation of these trajectories. Furthermore, we fuse these features and train a SVM classifier for recognition. We conduct some experiments on large vocabulary sign language dataset with up to 500 words and the results demonstrate the effectiveness of our proposed method. © Springer International Publishing AG 2016.

关键词： Trajectories

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Social Diffusion Analysis with Common-Interest Model for Image Annotation

Social Diffusion Analysis with Common-Interest Model for Ima...

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作者： Lei, Chenyi Liu, Dong Li, Weiping Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Automatic image annotation has been extensively studied, mostly from a content-based approach, whose effectiveness is restricted by the 'semantic gap' between low-level image features and semantic annotations, and by the irrelevance of annotations to image content. We propose a social diffusion analysis approach to image annotation, which exploits abundant social diffusion records about how images are disseminated within online social networks. Specifically, we propose a common-interest model to analyze social diffusion records, with the assumption that the diffusion pattern of an image in social networks is highly related to the relevance between image annotations and user preferences. In our proposed model, user preferences are represented as common interests of pairwise users instead of individual user interests. We find the notion of common interests not only facilitates the analysis of social diffusion patterns, but also leads to more accurate profiling of user preferences compared to individual interests. Based on the common-interest model, we design an image annotation framework via social diffusion analysis, which consists of the mining of common interests from social diffusion records, the feature extraction from diffusion graphs and common interests, and the automatic annotation by the learning-to-rank method. Experimental results on real-world data sets show that our proposed common-interest based approach outperforms individual-interest based methods, and also achieves superior performance than state-of-the-art content-based image annotation methods. © 2016 IEEE.

关键词： Diffusion

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An accurate SAR imaging method based on generalized minimax concave penalty 12

An accurate SAR imaging method based on generalized minimax ...

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12th European Conference on Synthetic Aperture Radar, EUSAR 2018

作者： Wei, Zhonghao Zhang, Bingchen Han, Bing Xu, Zhilin Hong, Wen Wu, YiRong University of Chinese Academy of Science and Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences China Laboratory of Technology in Geo-spatial Information Processing and Application Systems Institute of Electronics Chinese Academy of Sciences China Institute of Electronics Chinese Academy of Sciences China

ISBN: (纸本)9783800746361

Sparse signal processing has been applied in synthetic aperture radar (SAR) imaging successfully. As a typical sparse reconstruction model, L1 regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. The underestimation will cause the errors in RCS estimations. In this paper, we present a SAR imaging method based on generalized minimax convex (GMC) penalty, which can avoid the pixel intensity underestimation. The performance of the proposed method is verified using simulations and real data. © VDE VERLAG GMBH Â Berlin Â Offenbach.

关键词： Radar cross section

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BURST-LDA: A NEW TOPIC MODEL FOR DETECTING BURSTY TOPICS FROM STREAM TEXT

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Journal of Electronics(China) 2014年第6期31卷 565-575页

作者： Qi Xiang Huang Yu Chen Ziyan Liu Xiaoyan Tian Jing Huang Tinglei Wang Hongqi Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.

关键词： Text mining Burst detection Topic model Graphical model Bayesian inference

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NON-PARAMETRIC TOPIC MODEL FOR DISCOVERING geoGRAPHICAL TOPIC VARIATIONS

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Journal of Electronics(China) 2014年第6期31卷 576-586页

作者： Qi Xiang Huang Yu Song Jun Huang Tinglei Wang Hongqi Fu Kun Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

This paper presents a non-parametric topic model that captures not only the latent topics in text collections, but also how the topics change over space. Unlike other recent work that relies on either Gaussian assumptions or discretization of locations, here topics are associated with a distance dependent Chinese Restaurant Process(ddC RP), and for each document, the observed words are influenced by the document's GPS-tag. Our model allows both unbound number and flexible distribution of the geographical variations of the topics' content. We develop a Gibbs sampler for the proposal, and compare it with existing models on a real data set basis.

关键词： Text mining Topic model geographical topics Bayesian non-parameter

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Retrieval of Significant Wave Height Under Typhoon Conditions from Gaofen-3 SAR Imagery

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Journal of Ocean University of China 2022年第1期21卷 81-90页

作者： WANG Xiaochen HAN Bing ZHONG Lihua YUAN Xinzhe Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100190China Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research InstituteChinese Academy of ScienceBeijing 100190China The National Satellite Ocean Applications Center Beijing 100081China

The objective of this paper is to propose an empirical method to inverse significant wave height(SWH)under typhoon conditions from collected dual-polarization Gaofen(GF)-3 synthetic aperture radar(SAR)*** typhoon scenes were cap-tured from narrow scan(NSC)and wide scan(WSC)images,and collocated with European Center for Medium-Range Weather Fore-casts reanalysis data of(ECMWF).To improve the quality of GF-3 SAR images,the recalibration over rainforest and de-scalloping were carried *** establish the empirical relationship between SAR-derived parameters and collocated SWH,the sensitivity analysis of typical parameters about the normalized radar cross section(Nrcs)and imagery variance(Cvar)were performed to both VV and VH polarized *** scenes from GF-3 SAR imagery under typhoon conditions were used for training the model by the multivari-ate least square regression,and one scene was used for preliminary *** was found that the joint retrieval model based on VV and VH polarized SAR imagery performed better than any single polarized *** results,verified by using ECMWF data,revealed the soundness of this approach,with a correlation of 0.95,bias of 0 m,RMSE of 0.44 and SI of 0.01 when VV polarization and VH polarization data were both used.

关键词： Gaofen-3 SAR typhoon significant wave height

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