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arXiv 2018年

作者： He, Anfeng Luo, Chong Tian, Xinmei Zeng, Wenjun CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China Microsoft Research Beijing China

Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking. SA-Siam is composed of a semantic branch and an appearance branch. Each branch is a similarity-learning Siamese network. An important design choice in SA-Siam is to separately train the two branches to keep the heterogeneity of the two types of features. In addition, we propose a channel attention mechanism for the semantic branch. Channel-wise weights are computed according to the channel activations around the target position. While the inherited architecture from SiamFC [3] allows our tracker to operate beyond real-time, the twofold design and the attention mechanism significantly improve the tracking performance. The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Effects of residual motion compensation errors on the performance of airborne along-track interferometric SAR

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Frontiers of information technology & Electronic Engineering 2016年第10期17卷 1095-1105页

作者： Hui ZHANG Jun HONG Xiao-lan QIU Ji-chuan LI Fang-fang LI Feng MING Institute of Electronics Chinese Academy of Sciences Beoing 100190 China University of the Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Beijing 100190 China Science and Technology on Millimeter-Wave Laboratory Beijing 100854 China

Two approximations, center-beam approximation and reference digital elevation model （DEM） approximation, are used in synthetic aperture radar （SAR） motion compensation procedures. They usually introduce residual motion compensation errors for airborne single-antenna SAR imaging and SAR interferometry. In this paper, we investigate the effects of residual uncompensated motion errors, which are caused by the above two approximations, on the performance of airborne along-track interferometric SAR （ATI-SAR）. The residual uncompensated errors caused by center-beam approximation in the absence and in the presence of elevation errors are derived, respectively. Airborne simulation parameters are used to verify the correctness of the analysis and to show the impacts of residual uncompensated errors on the interferometric phase errors for ATI-SAR. It is shown that the interferometric phase errors caused by the center-beam approximation with an accurate DEM could be neglected, while the interferometric phase errors caused by the center-beam approximation with an inaccurate DEM cannot be neglected when the elevation errors exceed a threshold. This research provides theoretical bases for the error source analysis and signal processing of airborne ATI-SAR.

关键词： Synthetic aperture radar （SAR） Along-track interferometric Motion compensation Residual error Interferometric phase

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Optimization approach for multi-scale segmentation of remotely sensed imagery under k-means clustering guidance

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Cehui Xuebao/Acta Geodaetica et Cartographica Sinica 2015年第5期44卷 526-532页

作者： Wang, Huixian Jin, Huijia Wang, Jiaolong Jiang, Wanshou State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China Hebei Provincial Institute of Cartography Shijiazhuang050031 China

In order to adapt different scale land cover segmentation, an optimized approach under the guidance of k-means clustering for multi-scale segmentation is proposed. At first, small scale segmentation and k-means clustering are used to process the original images;then the result of k-means clustering is used to guide objects merging procedure, in which Otsu threshold method is used to automatically select the impact factor of k-means clustering;finally we obtain the segmentation results which are applicable to different scale objects. FNEA method is taken for an example and segmentation experiments are done using a simulated image and a real remote sensing image from GeoEye-1 satellite, qualitative and quantitative evaluation demonstrates that the proposed method can obtain high quality segmentation results. ©, 2015, SinoMaps Press. All right reserved.

关键词： Remote sensing

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User specific friend recommendation in social media community

User specific friend recommendation in social media communit...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Cong Guo Xinmei Tian Tao Mei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China Microsoft Research Beijing China

Social networks nowadays have become an important form of communication in which users can post their current status or share their lives by mobile phones or the Web. In this paper, we develop an effective and efficient model to estimate continuous tie strength between users for friend recommendation with the heterogeneous data from social media community. We categorize those multimodal data into two classes: interaction data (e.g., comments, marking favorite photos) and similarity data(e.g., common friends, groups, tags, geo, visual). We propose to use asymmetric relationship in the interaction data for tie strength estimation instead of using the conventional symmetric ones. Furthermore, by exploring the behavior of users in a social media community, we find that the tie strength between users can be approximately modeled as a linear function of their social connections. Based on this observation, we propose an effective and highly efficient user specific linear model for the tie strength estimation. The experiments on a popular social network show promising results and demonstrate the effectiveness of our proposed method.

关键词： Kernel Social network services Visualization Communities Data models Estimation Vectors

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GLOBAL PATCH MATCHING

GLOBAL PATCH MATCHING

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ISPRS Geospatial Week 2017

作者： Huang, X. Hu, K. Ling, X. Zhang, Y. Lu, Z. Zhou, G. Wuhan Engineering Science and Technology Institute Jiangda Road 30 Wuhan China Wuhan Sense Tour Tecnhonogy Company Luoshi South Road 320 Wuhan China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China School of Remote Sensing and Information Engineering Wuhan University China

This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise continuous. Firstly, simple linear iterative cluster method (SLIC) is used to segment the base image into a series of patches. Then, a global energy function, which consists of a data term and a smoothness term, is built on the patches. The data term is the second-order Taylor expansion of correlation coefficients, and the smoothness term is built by combing connectivity constraints and the coplanarity constraints are combined to construct the smoothness term. Finally, the global energy function can be built by combining the data term and the smoothness term. We rewrite the global energy function in a quadratic matrix function, and use least square methods to obtain the optimal solution. Experiments on Adirondack stereo and Motorcycle stereo of Middlebury benchmark show that the proposed method can remove fronto-parallel bias effectively, and produce continuous smooth surfaces. © Authors 2017.

关键词： 3D Label Algorithm Fronto-parallel Bias Global Optimization Matrix Energy Function Patch Matching

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CONVOLUTIONAL NEURAL NETWORK-BASED ARITHMETIC CODING OF DC COEFFICIENTS FOR HEVC INTRA CODING

CONVOLUTIONAL NEURAL NETWORK-BASED ARITHMETIC CODING OF DC C...

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IEEE International Conference on Image processing

作者： Changyue Ma Dong Liu Xiulian Peng Feng Wu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China

In the state-of-the-art video coding standard-High Efficiency Video Coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding tool. In CABAC, the binarization processes are manually designed, and the context models are empirically crafted, both of which incur that the probability distribution of the syntax elements may not be estimated accurately, and restrict the coding efficiency. In this paper, we adopt a convolutional neural network-based arithmetic coding (CNNAC) strategy, and conduct studies on the coding of the DC coefficients for HEVC intra coding. Instead of manually designing binarization process and context model, we propose to directly estimate the probability distribution of the value of the DC coefficient using densely connected convolutional networks. The estimated probability together with the real DC coefficient are then input into a multi-level arithmetic codec to fulfill entropy coding. Simulation results show that our proposed CNNAC leads to on average 22.47% bits saving compared with CABAC for the bits of DC coefficients, which corresponds to 1.6% BD-rate reduction than the HEVC anchor.

关键词： Context modeling Probability distribution Entropy coding Codecs Training data Tools

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Convolutional Neural Network-Based Invertible Half-Pixel Interpolation Filter for Video Coding

Convolutional Neural Network-Based Invertible Half-Pixel Int...

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IEEE International Conference on Image processing

作者： Ning Yan Dong Liu Houqiang Li Tong Xu Feng Wu Bin Li CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China

Fractional-pixel interpolation has been widely used in the modern video coding standards to improve the accuracy of motion compensated prediction. Traditional interpolation filters are designed based on the signal processing theory. However, video signal is non-stationary, making the traditional methods less effective. In this paper, we reveal that the interpolation filter can not only generate the fractional pixels from the integer pixels, but also reconstruct the integer pixels from the fractional ones. This property is called invertibility. Inspired by the invertibility of fractional-pixel interpolation, we propose an end-to-end scheme based on convolutional neural network (CNN) to derive the invertible interpolation filter, termed CNNInvIF. CNNlnvIF does not need the “ground-truth” of fractional pixels for training. Experimental results show that the proposed CNNInvIF can achieve up to 4.6% and on average 2.2% BD-rate reduction than HEVC under the low-delay P configuration.

关键词： Interpolation Video coding Training Image reconstruction Geometry Convolutional neural networks Image coding

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Identity Regularized Sparse Representation for Automatic Target Recognition in Sar Images

Identity Regularized Sparse Representation for Automatic Tar...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zongxu Pan Lei Liu Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

An identity regularized sparse representation (IRSR) based SAR target recognition method is proposed in this paper. The method aims to find a transformation that can map the data to a transformed space, in which targets from the same class are close with each other, no matter the distance of them in the original space. This identity constraint can be formulated as a ℓ 1 -norm minimization problem. By decoupling the problem into the sparse coding problem and the dictionary learning problem, the solution can be obtained iteratively. The solution is simply the weighted average of the sparse coding of all training data. Experimental results demonstrate that the proposed method is superior to several related methods.

关键词： Synthetic aperture radar Target recognition Linear programming Airplanes Remote sensing Support vector machines Minimization

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Doppler walk rectification based on KWT in passive radar

Doppler walk rectification based on KWT in passive radar

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xin Guan Li-Hua Zhong Dong-Hui Hu Chi-Biao Ding Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509033331

Doppler walk is introduced by high speed motion of targets in passive radar, which decreases the signal-to-noise ratio. In this paper, a Doppler walk rectification method based on keystone-Wigner-Ville transform (KWT) is proposed, and the interference, weights and noise impact are analyzed. No prior information and parameters searching are unnecessary.

关键词： Doppler effect Interference Transforms Frequency modulation Object detection Passive radar Filtering

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Combined analysis of probability of missed detection and positioning accuracy for receiver selection in SFN based passive radar systems

Combined analysis of probability of missed detection and pos...

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International Conference on Signal processing Proceedings (ICSP)

作者： Xiao-bo Li Xin Guan Li-hua Zhong Dong-hui Hu Chi-biao Ding Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479921898

Multistatic passive radar systems provide higher positioning accuracy comparing with the bistatic ones. However, the positioning performance of the multistatic passive radar systems is largely dependent on the geometry. Especially, it is difficult to designate transmitters from the given ones in a single frequency network (SFN) based passive radar system. Therefore the receiver location is of crucial importance. In this paper, the influence of different receiver positions for the probability of missed detection and the target positioning accuracy are analyzed. Then a performance function for selecting the optimal receiver is proposed. Finally, a simulation with three transmitters and one receiver is presented.

关键词： Accuracy Passive radar Receiving antennas Radio transmitters

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