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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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Research on statistical analysis of SAR images

Research on statistical analysis of SAR images

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Zhan, Qian-Qian You, Hong-Jian Hong, Wen Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 China Graduate School of the Chinese Academy of Sciences Beijing 100039 China National Key Laboratory of Microwave Imaging Technology Beijing 100190 China

ISBN: (纸本)9781424427314

The statistic distribution of SAR image plays an important role in SAR image processing, and it has a great influence on the results. In practical applications, the distribution is often specified by human through experience, but in fact the distribution changes with different pictures of different scenes. In this paper we first find a way to determine the form of the image distribution with Pearson System, and then we use the Chebyshev-Hermite polynomials and the statistical properties of SAR image to get the Edgeworth expansion. Finally, we practice the distribution simulation on four different types of scene (the ocean, the town, the mountain and the agricultural fields). The experimental results prove the efficiency and the accuracy of these two methods and will lay a good foundation for the follow-up steps in image processing. ©2009 IEEE.

关键词： Synthetic aperture radar

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DA-RefineNet: Dual-inputs Attention RefineNet for Whole Slide Image Segmentation

DA-RefineNet: Dual-inputs Attention RefineNet for Whole Slid...

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International Conference on Pattern Recognition

作者： Ziqiang Li Rentuo Tao Qianrun Wu Bin Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems University of Science and Technology of China Hefei Anhui China School of foreign studies Hefei University of Technology Hefei Anhui China

Automatic medical image segmentation has wide applications for disease diagnosing. However, it is much more challenging than natural optical image segmentation due to the high-resolution of medical images and the corresponding huge computation cost. The sliding window is a commonly used technique for whole slide image (WSI) segmentation, however, for these methods based on the sliding window, the main drawback is lacking global contextual information for supervision. In this paper, we propose a dual-inputs attention network (denoted as DA-RefineNet) for WSI segmentation, where both local fine-grained information and global coarse information can be efficiently utilized. Sufficient comparative experiments are conducted to evaluate the effectiveness of the proposed method, the results prove that the proposed method can achieve better performance on WSI segmentation compared to methods relying on single-input.

关键词： Knowledge engineering Image segmentation Semantics Breast Feature extraction Optical imaging Pattern recognition

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Robust and accurate mobile visual localization and its applications

Robust and accurate mobile visual localization and its appli...

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作者： Liu, Heng Mei, Tao Li, Houqiang Luo, Jiebo Li, Shipeng CAS Key Laboratory of Spatial Information Processing and Applied Systems University of Science and Technology of China Hefei 230027 China Microsoft Research Asia Beijing 100080 China University of Rochester Rochester NY 14627 United States

Mobile applications are becoming increasingly popular. More and more people are using their phones to enjoy ubiquitous location-based services (LBS). The increasing popularity of LBS creates a fundamental problem: mobile localization. Besides traditional localization methods that use GPS or wireless signals, using phone-captured images for localization has drawn significant interest from researchers. Photos contain more scene context information than the embedded sensors, leading to a more precise location description. With the goal being to accurately sense real geographic scene contexts, this article presents a novel approach to mobile visual localization according to a given image (typically associated with a rough GPS position). The proposed approach is capable of providing a complete set of more accurate parameters about the scene geo-context including the real locations of both the mobile user and perhaps more importantly the captured scene, as well as the viewing direction. To figure out how to make image localization quick and accurate, we investigate various techniques for large-scale image retrieval and 2D-to- 3D matching. Specifically, we first generate scene clusters using joint geo-visual clustering, with each scene being represented by a reconstructed 3D model from a set of images. The 3D models are then indexed using a visual vocabulary tree structure. Taking geo-tags of the database image as prior knowledge, a novel location-based codebook weighting scheme proposed to embed this additional information into the codebook. The discriminative power of the codebook is enhanced, thus leading to better image retrieval performance. The query image is aligned with the models obtained from the image retrieval results, and eventually registered to a real-world map. We evaluate the effectiveness of our approach using several large-scale datasets and achieving estimation accuracy of a user's location within 13 meters, viewing direction within 12 degrees, and view

关键词： Location based services

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THE REMOTE SENSING IMAGE geoMETRICAL MODEL of BP NEURAL NETWORK

THE REMOTE SENSING IMAGE GEOMETRICAL MODEL of BP NEURAL NETW...

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2020 International Conference on geomatics in the Big Data Era, ICGBD 2020

作者： Yue, C.Y. Sun, T. Xie, J.F. Beijing Institute of Space Mechanics and Electricity Beijing China Beijing Key Laboratory of Advanced Optical Remote Sensing Technology Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Land Satellite Remote Sensing Application Center Ministry of Natural Resources of P. R. China Beijing China

Imagery geometry models (IGMs) of the high-resolution satellite images (HRSIs) are always of great interest in the photogrammetry and remote sensing community for the raising new kinds of sensors and imaging systems. Especially the generalized sensor models (GSMs) have been widely used for positioning of satellite images, and the accuracy are already validated. Since Back propagation (BP) neural network is a better choice for the two key reasons of the replacement of physical sensor models by generalized sensor models, numerous mathematical estimations for every specialized sensor, and secret equations of the IGMs. Experiments are carried out to test the approximation accuracy of the new generalized sensor model. And the experimental results show that, the BP neural network is of extremely high accuracy for satellite imagery photogrammetric restitution. © 2020 C. Y. Yue et al.

关键词： Backpropagation

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Residual objectness for imbalance reduction

arXiv

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arXiv 2019年

作者： Chen, Joya Liu, Dong Luo, Bin Peng, Xuezheng Xu, Tong Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Tencent TaiQ Team

For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds. While several sampling/reweighting schemes have been explored to alleviate the imbalance, they are usually heuristic and demand laborious hyper-parameters tuning, which is hard to achieve the optimality. In this paper, we first reveal that such the imbalance could be addressed in a learning-based manner. Guided by this illuminating observation, we propose a novel Residual Objectness (ResObj) mechanism that addresses the imbalance by end-to-end optimization, while no further hand-crafted sampling/reweighting is required. Specifically, by applying multiple cascaded objectness-related modules with residual connections, we formulate an elegant consecutive refinement procedure for distinguishing the foregrounds from backgrounds, thereby progressively addressing the imbalance. Extensive experiments present the effectiveness of our method, as well as its compatibility and adaptivity for both region-based and one-stage detectors, namely, the RetinaNet-ResObj, YOLOv3-ResObj and FasterRCNN-ResObj achieve relative 3.6%, 3.9%, 3.2% Average Precision (AP) improvements compared with their vanilla models on COCO, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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Circuit tolerance design by differential evolution with hybrid analysis method

Circuit tolerance design by differential evolution with hybr...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Fugui Zhong Bin Li Bo Yuan CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems University of Science and Technology of China Hefei China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI) University of Science and Technology of China Hefei China USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI) University of Science and Technology of China Hefei China

With the continued scaling down of electronic device dimensions, circuit design under parameter variations has received increasing interests. In this paper, a new method that combine the differential evolution with hybrid analysis method is presented to solve the worst-case circuit tolerance design problem. The hybrid analysis method is comprised of two commonly used worst-case circuit tolerance analysis approaches, vertex analysis and Monte Carlo analysis. The search direction of differential evolution is leaded by vertex analysis at the first stage, through which we can reduce the computational complexity of fitness calculation dramatically. Monte Carlo analysis, a higher accuracy analysis method, is applied to ensure the quality of the solutions at the second stage. Some of the individuals are reinitialized to enhance the diversity of the population at the beginning of the second stage. By cooperating the two analysis methods, the proposed method can converge to the global optimum or near-optimum solutions more quickly. The experiment results show the effectiveness and efficiency of proposed techniques for the circuit tolerance design.

关键词： Monte Carlo methods Algorithm design and analysis Sociology Uncertainty Upper bound Computational efficiency

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Multi-scale semantic model for unsupervised object segmentation

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2012 2nd International Conference on Materials Science and information technology, MSIT 2012

作者： Li, Yu Li, Xiangjuan Zhang, Yasen Sun, Xian Wang, Hongqi Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783037854389

It is difficult to segment instances of object classes accurately unsupervised in images, because of the complexity of structures, inter-class differences, background interference and so on. A multi-scale semantic model method is proposed to overcome the disadvantages existing in most of the relative methods. This method uses generative model to deal with the objects obtained by multi-scale segmentations instead of whole image, and calculates kinds of visual features to mine the topic information of every object. In the segmentation process, a semantic correlative function of every segment block based on KL divergence is built up and minimized to select the object correct regions. Experimental results demonstrate the effectiveness of the proposed method. © (2012) Trans Tech Publications, Switzerland.

关键词： Semantics

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3D reconstruction and error analysis of multi-view space-borne SAR images under different configurations

3D reconstruction and error analysis of multi-view space-bor...

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IET International Radar Conference 2018, IRC 2018

作者： Wang, Chao Qiu, Xiaolan Li, Fangfang Lei, Bin University of the Chinese Academy of Sciences Beijing China Institute of Electrics Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electrics Chinese Academy of Sciences Beijing China

Synthetic aperture radar (SAR) has a good ability to detect the microwave scattering characteristics of the target and has a good capability of slant range Doppler positioning. Using multi-view SAR images in combination with image matching and positioning equations, the 3D position of the target can be obtained. In the past, multi-view 3D reconstruction mainly used side-looking images with parallel trajectories but different incident angles. That method is not universal for different configurations and lacks analysis of the relationship between solution and parameter error. This study aims at the problem of multi-view SAR 3D reconstruction. The authors establish general 3D reconstruction equations that can be used for non-parallel track and non-side-looking and the solution method is deduced. Based on this, an analysis method of error sensitivity is proposed and the dependence relationship between the reconstruction error and configuration, velocity, position, and slant range is analysed. The correctness of this method is verified by simulation experiments, which provides guidance for selection of configuration and sensor accuracy when applying 3D reconstruction. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Synthetic aperture radar

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Multi-objects detection in remote sensing images using multiple kernel learning

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2012 2nd International Conference on Materials Science and information technology, MSIT 2012

作者： Li, Xiangjuan Sun, Hao Zheng, Xinwei Sun, Xian Wang, Hongqi Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783037854389

The objective of this work is multiple objects detection in remote sensing images. Many classifiers have been proposed to detect military objects. In this paper, we demonstrate that linear combination of kernels can get a better classification precision than product of kernels. Starting with base kernels, we obtain different weights for each class through learning. Experiment on Caltech-101 dataset shows the learnt kernels yields superior classification results compared with single-kernel SVM. While such a powerful classifier act as a sliding-window detector to search planes in images collected from Google Earth, results shows the effectiveness of using MKL detector to locate military objects in remote sensing images. © (2012) Trans Tech Publications, Switzerland.

关键词： Object detection

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