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Deep Unrestricted Document Image Rectification

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arXiv 2023年

作者： Feng, Hao Liu, Shaokai Deng, Jiajun Zhou, Wengang Li, Houqiang The 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 Zhangjiang Laboratory Shanghai China The University of Adelaide Australian Institute for Machine Learning Australia

In recent years, tremendous efforts have been made on document image rectification, but existing advanced algorithms are limited to processing restricted document images, i.e., the input images must incorporate a complete document. Once the captured image merely involves a local text region, its rectification quality is degraded and unsatisfactory. Our previously proposed DocTr, a transformer-assisted network for document image rectification, also suffers from this limitation. In this work, we present DocTr++, a novel unified framework for document image rectification, without any restrictions on the input distorted images. Our major technical improvements can be concluded in three aspects. Firstly, we upgrade the original architecture by adopting a hierarchical encoder-decoder structure for multi-scale representation extraction and parsing. Secondly, we reformulate the pixel-wise mapping relationship between the unrestricted distorted document images and the distortion-free counterparts. The obtained data is used to train our DocTr++ for unrestricted document image rectification. Thirdly, we contribute a real-world test set and metrics applicable for evaluating the rectification quality. To our best knowledge, this is the first learning-based method for the rectification of unrestricted document images. Extensive experiments are conducted, and the results demonstrate the effectiveness and superiority of our method. We hope our DocTr++ will serve as a strong baseline for generic document image rectification, prompting the further advancement and application of learning-based algorithms. The source code and the proposed dataset are publicly available at https://***/fh2019ustc/DocTr-Plus. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods

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arXiv 2022年

作者： Li, Qing Zhou, Wengang Lu, Zhenbo Li, Houqiang The 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 The Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230000 China

Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive performance in continuous control tasks. However, they still suffer two nontrivial obstacles, i.e., low sample efficiency and overestimation bias. To this end, we propose Simultaneous Double Q-learning with Conservative Advantage Learning (SDQ-CAL). Our SDQ-CAL boosts the Double Q-learning for off-policy actor-critic RL based on a modification of the Bellman optimality operator with Advantage Learning. Specifically, SDQ-CAL improves sample efficiency by modifying the reward to facilitate the distinction from experience between the optimal actions and the others. Besides, it mitigates the overestimation issue by updating a pair of critics simultaneously upon double estimators. Extensive experiments reveal that our algorithm realizes less biased value estimation and achieves state-of-the-art performance in a range of continuous control benchmark tasks. We release the source code of our method at: https://***/LQNew/SDQ-CAL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Patch reordering: A novel way to achieve rotation and translation invariance in convolutional neural networks

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arXiv 2019年

作者： Shen, Xu Tian, Xinmei 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 Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia

Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks. However, the combination of convolution and pooling operations only shows invariance to small local location changes in meaningful objects in input. Sometimes, such networks are trained using data augmentation to encode this invariance into the parameters, which restricts the capacity of the model to learn the content of these objects. A more efficient use of the parameter budget is to encode rotation or translation invariance into the model architecture, which relieves the model from the need to learn them. To enable the model to focus on learning the content of objects other than their locations, we propose to conduct patch ranking of the feature maps before feeding them into the next layer. When patch ranking is combined with convolution and pooling operations, we obtain consistent representations despite the location of meaningful objects in input. We show that the patch ranking module improves the performance of the CNN on many benchmark tasks, including MNIST digit recognition, large-scale image recognition, and image retrieval. The code is available at https://***//jasonustc/caffe-multigpu/tree/TICNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

Outage Analysis of Dual-Hop Transmission with Buffer Aided Amplify-and-Forward Relay

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Outage Analysis of Dual-Hop Transmission with Buffer Aided A...

IEEE Vehicular technology Conference

作者： Guan-Xing Li Chen Dong Dong Liu Guangtao Li Yonghui Zhang China Mobile Group Liaoning Ltd. School of ECS University of Southampton CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China RF Micro Devices Co. Ltd.

ISBN: (纸本)9781479944484

A new packet-selection based transmission scheme is proposed on a dual-hop link with buffer aided amplify-and-forward relay, where the relay with a buffer can record the channel information of source-relay hop and store the received packets. In order to satisfy the constraint of equivalent end-to-end SNR, the relay may pick up a packet in the buffer to transmit, with consideration of the channel statuses of both the source-relay hop and the relay-destination hop. Therefore, by adjusting the packet transmission order in the relay, the outage probability can be decreased in the proposed transmission scheme. Both theoretical analyses and simulation results show that the proposed scheme outperforms traditional ones by a significant margin of 2-4 dB gain in terms of outage probability.

关键词： Relays Signal to noise ratio Fading Tin Delays Wireless communication Buffer storage

A Cloaking-Based Approach to Protect Location Privacy in Location-Based Services

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A Cloaking-Based Approach to Protect Location Privacy in Loc...

第三十三届中国控制会议

作者： Zheng Jiangyu Tan Xiaobin Cliff Zou Niu Yukun Zhu Jin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Department of Automation University of Science and Technology of China Department of Electrical Engineering and Computer Science University of Central FloridaOrlandoFL 32816-2362

ISBN: (纸本)9781479947249

With the widespread use of mobile devices, the location-based service(LBS) applications become increasingly popular, which introduces the new security challenge to protect user's location privacy. On one hand, a user expects to report his own location as far as possible away from his real location to protect his location privacy. On the other hand, in order to obtain high quality of service(QoS), users are required to report their locations as accurate as possible. To achieve the dedicated tradeoff between privacy requirement and QoS requirement, we propose a novel approach based on cloaking technique. We also discuss the disadvantage of the traditional general system model and propose an improved model. The basic idea of our approach is to select a sub-area from the generated cloaking area as user's reported location. The sub-area may not contain a user's real location,which prevents an adversary from performing attack with side information. Specifically, by defining an objective function with a novel location privacy metric and a QoS metric, we are able to convert the privacy issue to an optimization problem. Then, location privacy metric and QoS metric are given. To reduce the complexity of the optimization, a heuristic algorithm is *** privacy-preserving analysis and comparison with related work [8], we demonstrate the effectiveness and efficiency of our approach.

关键词： Location-based Services,Location Privacy,k-anonymity,Cloaking Area

Developing a geographic case-Based Reasoning approach

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Journal of Environmental Science and Engineering 2007年第1期1卷 1-7,18页

作者： DU Yun-yan ZHOU Cheng-hu SU Fen-zhen SHI Wen-zhong The State Key Lab of Environment and Information System Institute of Geography Environment and Resources CAS Beijing 100101 China Advanced Research Centre for Spatial Information Technology Department of Land-Surveying and Geo-lnformatics the Hong Kong Polytechnic University

case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This paper intends to further extend current CBR to a geographic CBR （geo-CBR）. First, the concept of geo-CBR is proposed. Second, a representation model for geographic cases has been established based on the Tesseral model and on a further extension in spatio-temporal dimensions for geographic cases. Third, a reasoning model for geo-CBR is developed by considering the spatio-temporat characteristics and the uncertain and limited information of geographic cases. Finally, the geo-CBR model is applied to forecasting the production of ocean fisheries to demonstrate the applicability of the developed geo-CBR in solving problems in the real world. According to the experimental results, geo-CBR is an effective and easy-to-implement approach for predicting geographic cases quantitatively.

关键词： case-Based Reasoning (CBR) geographic CBR (geo-CBR) representation model reasoning model Tesseral model

Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation

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arXiv 2023年

作者： Li, Ziqiang Wang, Chaoyue Rui, Xue Xue, Chao Leng, Jiaxu Li, Bin Big Data and Decision Lab University of Science and Technology of China China The University of Sydney Australia JD Explore Academic China School of computer science Chongqing University of Posts and Telecommunications China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System China

Few-shot image generation aims to train generative models using a small number of training images. When there are few images available for training (e.g. 10 images), Learning From Scratch (LFS) methods often generate images that closely resemble the training data while Transfer Learning (TL) methods try to improve performance by leveraging prior knowledge from GANs pre-trained on large-scale datasets. However, current TL methods may not allow for sufficient control over the degree of knowledge preservation from the source model, making them unsuitable for setups where the source and target domains are not closely related. To address this, we propose a novel pipeline called Peer is your Pillar (PIP), which combines a target few-shot dataset with a peer dataset to create a data-unbalanced conditional generation. Our approach includes a class embedding method that separates the class space from the latent space, and we use a direction loss based on pre-trained CLIP to improve image diversity. Experiments on various few-shot datasets demonstrate the advancement of the proposed PIP, especially reduces the training requirements of few-shot image generation. © 2023, CC BY.

关键词： Large dataset

Recent Advance in Content-based Image Retrieval: A literature survey

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arXiv 2017年

作者： Zhou, Wengang Li, Houqiang Fellow, Tian I.E.E.E. 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 Department of Computer Science University of Texas San Antonio San AntonioTX78249 United States

—The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Content based retrieval

A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with applications in Tomographic SAR Imaging

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arXiv 2022年

作者： Shi, Ruizhe Zhang, Zhe Qiu, Xiaolan Ding, Chibiao School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100190 China Suzhou Aerospace Information Research Institute Suzhou Key Laboratory of Intelligent Aerospace Big Data Application Technology Jiangsu Suzhou215123 China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Beijing100190 China

This paper presents a novel efficient method for gridless line spectrum estimation problem with single snapshot, namely the gradient descent least squares (GDLS) method. Conventional single snapshot (a.k.a. single measure vector or SMV) line spectrum estimation methods either rely on smoothing techniques that sacrifice the array aperture, or adopt the sparsity constraint and utilize compressed sensing (CS) method by defining prior grids and resulting in the off-grid problem. Recently emerged atomic norm minimization (ANM) methods achieved gridless SMV line spectrum estimation, but its computational complexity is extremely high;thus it is practically infeasible in real applications with large problem scales. Our proposed GDLS method reformulates the line spectrum estimations problem into a least squares (LS) estimation problem and solves the corresponding objective function via gradient descent algorithm in an iterative fashion with efficiency. The convergence guarantee, computational complexity, as well as performance analysis are discussed in this paper. Numerical simulations and real data experiments show that the proposed GDLS algorithm outperforms the state-of-the-art methods e.g., CS and ANM, in terms of estimation performances. It can completely avoid the off-grid problem, and its computational complexity is significantly lower than ANM. Our method has been tested in tomographic SAR (TomoSAR) imaging applications via simulated and real experiment data. Results show great potential of the proposed method in terms of better cloud point performance and eliminating the gridding effect. Copyright © 2022, The Authors. All rights reserved.

关键词： Spectrum analysis