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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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STGE-Former: Spatial-Temporal Graph-Enhanced Transformer for EEG-Based Major Depressive Disorder Detection

STGE-Former: Spatial-Temporal Graph-Enhanced Transformer for...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu Chen Chunfeng Yang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Applying deep learning techniques to Electroencephalogram (EEG) data has shown great potential in the field of depression detection. However, existing EEG-based depression detection models face challenges: they struggle to capture the complex spatiotemporal dependencies and the complementary nature of spatiotemporal information in EEG data; functional connectivity between brain regions is not sufficiently considered. To address these issues, we propose a new Spatial-Temporal Graph-Enhanced Transformer, named STGE-Former. Raw EEG signals are first mapped to Spatial-Temporal Shared Embeddings, then processed by the Spatial Attention Stream and the Temporal Graph-Enhanced Attention Stream to extract spatiotemporal complementary information, and finally classified through a classification head. Experimental results on the MODMA dataset show that our model outperforms existing methods in the task of EEG-Based MDD Detection. STGE-Former provides a promising approach for automatic depression detection. The code is available at https://***/RockyChen0205/STGE-Former.

关键词： Deep learning Electric potential Signal processing Depression Transformers Brain modeling Electroencephalography Spatiotemporal phenomena Speech processing Faces

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A parallel ant colonies approach to de novo prediction of protein backbone in CASP8/9

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Science China(information Sciences) 2013年第10期56卷 226-238页

作者： LV Qiang WU HongJie WU JinZhen HUANG Xu LUO XiaoHu QIAN PeiDe School of Computer Science and Technology Soochow University Jiangsu Provincial Key Lab for Information Processing Technologies School of Electronic and Information Engineering Suzhou University of Science and Technology

Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational *** modeling of the protein backbone represents the initial phase of a protein structure prediction *** a parallel ant colony optimization based on sharing one pheromone matrix,this report proposes a parallel approach to predict the structure of a protein *** parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy *** free modeling targets in CASP8/9 are used to evaluate the performance of the *** 13 targets in CASP8,two out of the predicted model1s selected by our approach are the best of the published CASP8 results,and seven out of the model1s are ranked in the top *** 29 targets in CASP9,20 out of the best models from our predictions are ranked in the top 10,and 11 out of the model1s are ranked in the top 10.

关键词： protein backbone prediction parallel ant colony optimization

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A Fast Calculation of Metric Scores for Learning Bayesian Network

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International Journal of Automation and computing 2012年第1期9卷 37-44页

作者： Qiang Lv Xiao-Yan Xia Pei-De Qian School of Computer Science and Technology Soochow University Suzhou 215006 PRC Jiangsu Provincial Key Lab for Computer Information Processing Technology Suzhou 215006 PRC

Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.

关键词： Frequent counting radix-based calculation ADtree learning Bayesian network metric score

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Prof. Rudolf Emil Kalman OBITUARY

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IEEE CONTROL SYSTEMS MAGAZINE 2017年第1期37卷 151-152页

作者： Antoulas, Athanasios Georgiou, Tryphon T. Khargonekar, Pramod P. Ozguler, A. Bulent Sontag, Eduardo D. Yamamoto, Yutaka JiangSu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University Suzhou China

Recounts the career and contributions of Professor Rudolf Emil Kalman.

关键词： Obituaries Kalman, Rudolf Emil

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Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning

arXiv

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arXiv 2025年

作者： He, Yuting Wang, Boyu Ge, Rongjun Chen, Yang Yang, Guanyu Li, Shuo Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing China School of Instrument Science and Engineering Southeast University Nanjing China Department of Computer Science Western University LondonONN6A 3K7 Canada Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We proposes a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels’ correspondence under the condition of topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via gradient. We also proposes a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code at a companion website. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Dependency-driven feature-based learning for extracting protein-protein interactions from biomedical text

Dependency-driven feature-based learning for extracting prot...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Liu, Bing Qian, Longhua Wang, Hongling Zhou, Guodong Jiangsu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

Recent kernel-based PPI extraction systems achieve promising performance because of their capability to capture structural syntactic information, but at the expense of computational complexity. This paper incorporates dependency information as well as other lexical and syntactic knowledge in a feature-based framework. Our motivation is that, considering the large amount of biomedical literature being archived daily, feature-based methods with comparable performance are more suitable for practical applications. Additionally, we explore the difference of lexical characteristics between biomedical and newswire domains. Experimental evaluation on the AIMed corpus shows that our system achieves comparable performance of 54.7 in F1-Score with other state-of-the-art PPI extraction systems, yet the best performance among all the feature-based ones.

关键词： Syntactics

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Dependency-driven anaphoricity determination for coreference resolution

Dependency-driven anaphoricity determination for coreference...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Kong, Fang Zhou, Guodong Qian, Longhua Zhu, Qiaoming JiangSu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

This paper proposes a dependency-driven scheme to dynamically determine the syntactic parse tree structure for tree kernel- based anaphoricity determination in coreference resolution. Given a full syntactic parse tree, it keeps the nodes and the paths related with current mention based on constituent dependencies from both syntactic and semantic perspectives, while removing the noisy information, eventually leading to a dependency-driven dynamic syntactic parse tree (D-DSPT). Evaluation on the ACE 2003 corpus shows that the D-DSPT outperforms all previous parse tree structures on anaphoricity determination, and that applying our anaphoricity determination module in coreference resolution achieves the so far best performance.

关键词： Syntactics

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Improve tree kernel-based event pronoun resolution with competitive information

Improve tree kernel-based event pronoun resolution with comp...

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22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Kong, Fang Zhou, Guodong JiangSu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

ISBN: (纸本)9781577355120

Event anaphora resolution plays a critical role in discourse analysis. This paper proposes a tree kernel- based framework for event pronoun resolution. In particular, a new tree expansion scheme is introduced to automatically determine a proper parse tree structure for event pronoun resolution by considering various kinds of competitive information related with the anaphor and the antecedent candidate. Evaluation on the OntoNotes English corpus shows the appropriateness of the tree kernel-based framework and the effectiveness of competitive information for event pronoun resolution.

关键词： Trees (mathematics)

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A tree kernel-based unified framework for Chinese zero anaphora resolution

A tree kernel-based unified framework for Chinese zero anaph...

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Conference on Empirical Methods in Natural Language processing, EMNLP 2010

作者： Kong, Fang Zhou, Guodong JiangSu Provincial Key Lab. for Computer Information Processing Technology School of Computer Science and Technology Soochow University China

ISBN: (纸本)1932432868

This paper proposes a unified framework for zero anaphora resolution, which can be divided into three sub-tasks: zero anaphor detection, anaphoricity determination and antecedent identification. In particular, all the three sub-tasks are addressed using tree kernel-based methods with appropriate syntactic parse tree structures. Experimental results on a Chinese zero anaphora corpus show that the proposed tree kernel-based methods significantly outperform the feature-based ones. This indicates the critical role of the structural information in zero anaphora resolution and the necessity of tree kernel-based methods in modeling such structural information. To our best knowledge, this is the first systematic work dealing with all the three sub-tasks in Chinese zero anaphora resolution via a unified framework. Moreover, we release a Chinese zero anaphora corpus of 100 documents, which adds a layer of annotation to the manually-parsed sentences in the Chinese Treebank (CTB) 6.0. © 2010 Association for Computational Linguistics.

关键词： Trees (mathematics)

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