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Road extraction by deep residual u-net

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arXiv 2017年

作者： Zhang, Zhengxin Liu, Qingjie Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

—Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model is two-fold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters however better performance. We test our network on a public road dataset and compare it with U-Net and other two state of the art deep learning based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts. Copyright © 2017, The Authors. All rights reserved.

关键词： Roads and streets

Building a Chinese discourse topic corpus with a micro-topic scheme based on theme-rheme theory

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Big Data Analytics 2017年第1期2卷 1-14页

作者： Xi, Xue-feng Zhou, Guodong Department of Computer Science and Technology Suzhou University of Science and Technology Suzhou China School of Computer Science and Technology Soochow University Suzhou China Virtual Reality Key Laboratory of Intelligent Interaction and Application Technology of Suzhou Suzhou China

How to build a suitable discourse topic structure is an important issue in discourse topic analysis, which is the core of natural language understanding. Not only is it the key basic unit to implement automatic computing, but also the key to realize the transformation from unstructured data to structured data during the process of big data analytics. Although the discourse topic structure has wide potential for application in discourse analysis and related tasks, the research on constructing such discourse resources is quite limited in Chinese language. In this paper, we propose a micro-topic scheme (MTS) to represent the discourse topic structure in the Chinese language according to theme-rheme theory, with elementary discourse topic unit(EDTU) as the node and referent of theme-rheme as link. In particular, thematic progression is employed to directly represent the development of the discourse topic structure. Guided by the MTS, we manually annotate a Chinese Discourse Topic Corpus (CDTC) of 500 documents. Moreover, we get 89.9 and 72.15 F1 value in two identification preliminary experiments, respectively, which show that the proposed representation can perform good automatic computation. The lack of the formal representation system and related corpus resources for Chinese discourse topic structure has greatly restricted the study of discourse topic analysis in natural language, and further affected the development of natural language understanding. To address the above issues, a micro-topic scheme(MTS) representation is proposed based on functional grammar theory, and the corresponding corpus resources(i.e., CDTC) are constructed. Our preliminary evaluation justifies the appropriateness of the MTS for Chinese discourse analysis and the usefulness of our CDTC.

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Correction to: Automatic differentiation of Glaucoma visual field from non-glaucoma visual field using deep convolutional neural network

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BMC medical imaging 2019年第1期19卷 40页

作者： Fei Li Zhe Wang Guoxiang Qu Diping Song Ye Yuan Yang Xu Kai Gao Guangwei Luo Zegu Xiao Dennis S C Lam Hua Zhong Yu Qiao Xiulan Zhang Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-sen University Guangzhou China. SenseTime Group Limited Hong Kong China. Guangdong key lab of Computer Vision & Virtual Reality Multimedia Research Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. C-MER Dennis Lam Eye Hospital Shenzhen China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming China. Department of Ophthalmology The First Affiliated Hospital of Kunming Medical University Kunming China. zhoculist@***. Guangdong key lab of Computer Vision & Virtual Reality Multimedia Research Center Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. yu.qiao@***. Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-sen University Guangzhou China. zhangxl2@***.

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Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer

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Chinese Medical Journal 2019年第4期132卷 379-387页

作者： Lei Ding Guang-Wei Liu Bao-Chun Zhao Yun-Peng Zhou Shuai Li Zheng-Dong Zhang Yu-Ting Guo Ai-Qin Li Yun Lu Hong-Wei Yao Wei-Tang Yuan Gui-Ying Wang Dian-Liang Zhang Lei Wang Shandong Key Laboratory of Digital Medicine & Computer Assisted Surgery Qingdao UniversityQingdaoShandong 266003China Department of General Surgery Qingdao Municipal HospitalQingdaoShandong 266011China Department of General Surgery The Sixth Affiliated Hospital of Sun Yat-Sen UniversityGuangzhouGuangdong 510655China. Department of Medical Administration Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China Department of Outpatient Administration Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China Department of Follow-up Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China Department of Gastroenterology Surgery Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China State Key Laboratory of Virtual Reality Technology &Systems Beihang UniversityBeijing 100191China Department of General Surgery Beijing Friendship HospitalCapital Medical University & National Clinical Research Center for Digestive DiseasesBeijing 100050China Department of General Surgery First Affiliated Hospital of Zhengzhou UniversityZhenzhouHenan 450052China Department of General Surgery Fourth Hospital of Hebei Medical UniversityShijiazhuangHebei 050011China

Background: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use. Methods: Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome;recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model. Results: Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.134, and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients. Conclusion: Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.

关键词： AI (Artificial Intelligence) Magnetic resonance imaging Pathology Lymph nodes Rectal cancer

A novel photometric stereo method with nonisotropic point light sources 23

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A novel photometric stereo method with nonisotropic point li...

23rd International Conference on Pattern Recognition, ICPR 2016

作者： Nie, Ying Zhan, Song Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781509048472

This paper presents a photometric stereo method with nonisotropic point light sources. Subject to the non-uniform lighting conditions produced by the nonisotropic point sources, each incident light ray should be precisely determined so as to realize an accurate calculation of surface normal. In the proposed method, radiance model of the light source is firstly introduced to the classical photometric stereo framework. By considering the distance and angular attenuations of incident light rays, a precise description for the lighting field can be established. Based on the initial 3D reconstruction result, an iterative process is introduced to optimize the primary light model parameters with respect to the unknown distance factor. The experimental setup is quite simple, which only consists of some LEDs and one camera. And the experimental results show that, with the proposed method, accuracy of the reconstructed surface normal can be greatly improved in comparison with some conventional light models. © 2016 IEEE.

关键词： Light sources

R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

Single shot text detector with regional attention

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arXiv 2017年

作者： He, Pan Huang, Weilin He, Tong Zhu, Qile Qiao, Yu Li, Xiaolin National Science Foundation Center for Big Learning University of Florida Department of Engineering Science University of Oxford Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN-based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allowing the detector to work reliably on multi-scale and multi-orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 benchmark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http://***/. Copyright © 2017, The Authors. All rights reserved.

关键词： Image enhancement

Low-resolution face recognition in the wild via selective knowledge distillation

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arXiv 2018年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Li, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security at University of Chinese Academy of Sciences China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9, 433 faces per second on GPU. Copyright © 2018, The Authors. All rights reserved.

关键词： Students

Direction-aware spatial context features for shadow detection

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arXiv 2017年

作者： Hu, Xiaowei Zhu, Lei Fu, Chi-Wing Qin, Jing Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Centre for Smart Health School of Nursing Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting shadows. This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate our network. Experimental results show that our network outperforms state-of-the-art methods and achieves 97% accuracy and 38% reduction on balance error rate. Copyright © 2017, The Authors. All rights reserved.

关键词： Recurrent neural networks