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arXiv 2019年

作者： Shen, Xu Tian, Xinmei He, Anfeng Sun, Shaoyan Tao, Dacheng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of images. Intuitively, with sufficient layers and parameters, hierarchical combinations of convolution (matrix multiplication and nonlinear activation) and pooling operations should be able to learn a robust mapping from transformed input images to transform-invariant representations. In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage. This prevents complex dependencies of specific rotation, scale, and translation levels of training images in CNN models. Rather, each convolutional kernel learns to detect a feature that is generally helpful for producing the transform-invariant answer given the combinatorially large variety of transform levels of its input feature maps. In this way, we do not require any extra training supervision or modification to the optimization process and training images. We show that random transformation provides significant improvements of CNNs on many benchmark tasks, including small-scale image recognition, large-scale image recognition, and image retrieval. The code is available at https: ACM ISBN 978-1-4503-2138-9. Copyright © 2019, The Authors. All rights reserved.

关键词： Image recognition

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Towards Hybrid-Optimization Video Coding

arXiv

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arXiv 2022年

作者： Huo, Shuai Liu, Dong Li, Li Ma, Siwei Wu, Feng Gao, Wen The Cas Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Institute of Digital Media Peking University Beijing100871 China

Video coding is a mathematical optimization problem of rate and distortion essentially. To solve this complex optimization problem in practice, two popular video coding frameworks have been developed: block-based hybrid video coding and end-to-end learned video coding. If we rethink video coding from the perspective of optimization, we find that the existing two frameworks represent two directions of optimization solutions. Block-based hybrid video coding represents the discrete optimization solution because those irrelevant coding modes are discrete in mathematics. The discrete solution provides multiple starting points (i.e. modes) in global optimization space and then searches for the best one among them. However, the search-based optimization algorithm is not efficient enough. On the other hand, end-to-end learned video coding represents the continuous optimization solution because the optimization algorithm of deep learning, gradient descent, is based on a continuous function. The continuous solution optimizes a group of model parameters efficiently by such a numerical algorithm. However, limited by only one starting point, it is easy to fall into the local optimum. To better solve the optimization problem, we propose a hybrid of discrete and continuous optimization video coding. We regard video coding as a hybrid of the discrete and continuous optimization problem, and use both search and numerical algorithm to solve it. Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently. Finally, we search for the global optimum among those local optimums. Guided by the hybrid optimization idea, we design a hybrid optimization video coding framework, which is built on continuous deep networks entirely and also contains some discrete modes. We conduct a comprehensive set of experiments to verify the efficiency of our hybrid optimization. Compared to the continuous opti

关键词： Deep neural networks

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An efficient four-parameter affine motion model for video coding

arXiv

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arXiv 2017年

作者： Li, Li Li, Houqiang Liu, Dong Yang, Haitao Lin, Sixin Chen, Huanbang Wu, Feng CAS Key Laboratory of Technology Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Media Technology Laboratory Central Research Institute Huawei Technologies Co. Ltd

In this paper, we study a simplified affine motion model based coding framework to overcome the limitation of translational motion model and maintain low computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., advanced affine motion vector prediction combined with a gradient-based fast affine motion estimation algorithm and affine model merge, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation, which reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation, which performs motion compensation at the block level rather than the pixel level to reduce the interpolation times. Our proposed techniques have been implemented based on the state-of-the-art high efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve on average 11.1% and 19.3% bits saving for random access and low delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both encoder and decoder are within an acceptable range. Copyright © 2017, The Authors. All rights reserved.

关键词： Motion estimation

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Phase Transition Analysis for Compressive Sensing Based DoA Estimation in Automotive Radar

Phase Transition Analysis for Compressive Sensing Based DoA ...

引用

International Radar Symposium (IRS)

作者： Yan Zhang Silin Gao Mingqian Liu Bingchen Zhang Yirong Wu Chinese Academy of Sciences Aerospace Information Research Institute Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Processing Application System Beijing China

ISBN: (数字)9788394942151

ISBN: (纸本)9781728157863

In automotive radar applications, the compressive sensing (CS) based DoA estimation is used in array signal processing in recent years. Sparse reconstruction has the potential to estimate the direction of arrival (DoA) with super resolution. However, failed results may be acquired via sparse reconstruction in inappropriate conditions, namely the critical condition determining success or failure must be taken into consideration. In this paper, the sparsity of the scenario and the signal-to-noise ratio (SNR) are analyzed as the main factors via phase transition diagrams. Other factors affecting the success or failure are also investigated, such as the array configuration and the sparse recovery algorithm. Simulated and experimental results demonstrate the critical conditions, in which the DoA estimation is successful or failed.

关键词： Direction-of-arrival estimation Estimation Radar applications Compressed sensing Signal resolution Signal to noise ratio Automotive engineering

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Analysis of phase preservation and interferometric offset test in sparse SAR imaging

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Science China(information Sciences) 2024年第2期67卷 219-231页

作者： Zhongqiu XU Bingchen ZHANG Guangzuo LI Xueli ZHAN Yanfei BAO Yirong WU Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences PLA Academy of Military Sciences

Sparse synthetic aperture radar(SAR) imaging has emerged as a reliable microwave imaging scheme in the recent decade and excels in down-sampling reconstruction and full-sampling performance improvements such as noise, sidelobe, speckle, and ambiguity suppression. To utilize complex image products of sparse reconstruction for improvement in polarimetric, interferometric, and tomographic SAR imaging, it is necessary to evaluate the phase preservation of sparse SAR imaging. In this study, we first introduce the general alternating direction method of multipliers(ADMM) as the universal framework for sparse reconstruction algorithms and adopt chirp scaling algorithm(CSA)-based azimuth-range decouple operators to avoid expensive data storage and processing. Further, we theoretically analyze the phase preservation of the sparse reconstruction algorithm through a comparison with the reconstruction results of CSA. Finally,we conduct the interferometric offset test on the sparse reconstruction results of simulated and real Gaofen-3(GF-3) SAR data, demonstrating the phase-preserving ability of sparse methods.

关键词： sparse SAR imaging phase preservation interferometric offset test general ADMM azimuth-range decouple

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A Novel Multi-Channel Phase Error Estimation Method Based On Stochastic Optimization For Tomographic Sar Autofocusing

A Novel Multi-Channel Phase Error Estimation Method Based On...

引用

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Muhan Wang Silin Gao Zhe Zhang Xiaolan Qiu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou Aerospace Information Research Institute Suzhou China University of Chinese Academy of Sciences Beijing China

Tomographic SAR (TomoSAR) technology has gained significant attention in recent years due to its three-dimensional imaging capability. However, in practical applications, phase errors between different channels can degrade the quality of three-dimensional imaging. Current state-of-the-art methods for phase error compensation based on autofocus techniques suffer from high computational complexity, making them unsuitable for large-scale three-dimensional imaging. In this paper, we propose a multi-channel phase error estimation method based on error back-propagation training optimization. By utilizing the TomoSAR model that incorporates phase errors from multiple channels, we construct a matrix containing the parameters to be estimated for inter-channel phase errors. Through stochastic gradient descent algorithm, we iteratively optimize the parameters of the phase error matrix, ultimately obtaining an estimation of the inter-channel phase errors. Experimental results validate the accuracy of the proposed method.

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Visual analyses of music history: A user-centric approach

arXiv

引用

arXiv 2017年

作者： Zhang, Jingxian Liu, Dong Media Lab Massachusetts Institute of Technology CambridgeMA02139 United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Music history, referring to the records of users' listening or downloading history in online music services, is the primary source for music service providers to analyze users' preferences on music and thus to provide personalized recommendations to users. In order to engage users into the service and to improve user experience, it would be beneficial to provide visual analyses of one user's music history as well as visualized recommendations to that user. In this paper, we take a user-centric approach to the design of such visual analyses. We start by investigating user needs on such visual analyses and recommendations, then propose several different visualization schemes, and perform a pilot study to collect user feedback on the designed schemes. We further conduct user studies to verify the utility of the proposed schemes, and the results not only demonstrate the effectiveness of our proposed visualization, but also provide important insights to guide the visualization design in the future. Copyright © 2017, The Authors. All rights reserved.

关键词： Visualization

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SFDFNet: Single-path Feature Deep Fusion Network for Fast Semantic Segmentation of SAR Images in Building Areas

SFDFNet: Single-path Feature Deep Fusion Network for Fast Se...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhang, Wenyi Dai, Xiangyu Chu, Qingwei Zhang, Jiande Ding, Hao Li, Guangzuo Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences No. 9 Dengzhuang South Road Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences No. 19 Yuquan Road Beijing China QiLu Aerospace Information Research Institute No. 44 Industry North Road Jinan China

ISBN: (纸本)9781839539954

With the SAR satellites have gradually become one of the most important methods of Earth observation, rapid interpretation of SAR images has become particularly important. However, the unique imaging mechanism of SAR makes its interpretation very difficult. In this paper, we propose the SFDFNet, a deep learning-based fast segmentation method for SAR image buildings, to address the problem of difficult SAR image interpretation. A bottleneck layer of buffered large receptive field is designed in the downsampling process. The unique design of the number of channels reduces the amount of computation without affecting the extraction of information. Meanwhile, improving on the ECA module, a two-layer ECA module was proposed to increase the receptive field and improve the linear expression. Experiments show that the proposed network has excellent results in segmentation accuracy and inference speed. © The Institution of Engineering & technology 2023.

关键词： Semantic Segmentation

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A visually inspired variational method for automatic image registration

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Cehui Xuebao/Acta geodaetica et Cartographica Sinica 2015年第8期44卷 893-899页

作者： Wang, Huixian Jin, Huijia Lei, Chengqiang Jiang, Wanshou Wang, Yan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Electronic & Optical Engineering Department Mechanical Engineering College Shijiazhuang050003 China Institute of Information in Remote Sensing in Beijing Beijing100192 China

A visually inspired variational method for automatic image registration is proposed to solve local deformation which traditional global registration model cannot well satisfy. The variational model considers local transformation, global smoothness and visual constraints. To account for intensity variations, we incorporate change of local contrast and brightness into our model. Firstly, the data entry of registration model is built according to the root-mean-square error of intensity;secondly, adaptive constraint using H1 half norm is used to ensure the global smooth in the model;finally, in order to make sure that the spatial attributes of the image satisfy the visual requirements and without distortion, the linear features are used as priori constraints. During the solution of model parameters, the whole image is used to globally estimate the transformation parameters, and then local estimation of the parameters is taken in a small neighbor. The entire procedure is built upon a multi-level differential framework, and the transformation parameters are calculated iteratively, which can consider both global smoothness and local distortion. To assess the quality of the proposed method, ZY-3 satellite images were used. Visual and quantitative analysis proved that the proposed method can significantly improve the registration precision. ©, 2015, SinoMaps Press. All right reserved.

关键词： Image registration

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Plug-And-Play TomoSAR Imaging With Prior information

Plug-And-Play TomoSAR Imaging With Prior Information

引用

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Kun Wang Jie Li Yizhe Fan Bingchen Zhang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Technology in Geo-Spatial Information Processing and Application System School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Synthetic aperture radar (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularization terms, including L 1 norm, total variation norm, and morphology term, have been introduced into the TomoSAR inversion model. However, there is a lack of attention to establishing a TomoSAR imaging framework with a modular structure that provides flexibility in specifically enhancing different features. In this article, a novel 3D tomographic reconstruction framework based on plug-and-play (PnP) priors and Alternating Direction Method of Multipliers (ADMM) is proposed. The PnP-ADMM framework achieves flexibility through the selection of appropriate priors for the features of targets, leading to a trade-off between the performance of feature enhancement and computational complexity. Simulation and real data experiments verify the effectiveness of the proposed method in flexibly selecting priors for feature enhancement of specific targets.

关键词： Radar remote sensing Three-dimensional displays Morphology Tomography Apertures Convex functions Computational complexity

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