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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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Learning bronchiole-sensitive airway segmentation cnns by feature recalibration and attention distillation 23rd

Learning bronchiole-sensitive airway segmentation cnns by fe...

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23rd International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China UdL INSA Lyon CREATIS CNRS UMR 5220 INSERM U1206 Lyon France

ISBN: (纸本)9783030597092

Training deep convolutional neural networks (CNNs) for airway segmentation is challenging due to the sparse supervisory signals caused by severe class imbalance between long, thin airways and background. In view of the intricate pattern of tree-like airways, the segmentation model should pay extra attention to the morphology and distribution characteristics of airways. We propose a CNNs-based airway segmentation method that enjoys superior sensitivity to tenuous peripheral bronchioles. We first present a feature recalibration module to make the best use of learned features. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce the airway-specific representation learning. High-resolution attention maps with fine airway details are passing down from late layers to previous layers iteratively to enrich context knowledge. Extensive experiments demonstrate considerable performance gain brought by the two proposed modules. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

arXiv

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arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： Computerized tomography

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Learning tubule-sensitive CNNs for pulmonary airway and artery-vein segmentation in CT

arXiv

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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： Computerized tomography

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CNN-based invertible wavelet scattering for the investigation of diffusion properties of the in vivo human heart in diffusion tensor imaging

arXiv

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arXiv 2019年

作者： Deng, Zeyu Wang, Lihui Kuai, Zixiang Chen, Qijian Cheng, Xinyu Yang, Feng Yang, Jie Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 LyonF-69621 France

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing. Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

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ICDAR 2013 Competition on Historical Book recognition (HBR 2013)

ICDAR 2013 Competition on Historical Book Recognition (HBR 2...

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International Conference on Document analysis and recognition

作者： A. Antonacopoulos C. Clausner C. Papadopoulos S. Pletschacher Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Manchester UK

This paper presents an objective comparative evaluation of layout analysis and recognition methods for scanned historical books. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2013 and the 2nd International Workshop on Historical Document Imaging and Processing (HIP2013), presenting the results of the evaluation of five methods - three submitted and two state-of-the-art systems (one commercial and one open-source). Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions, one evaluating segmentation and region classification (with a text extraction goal) and the other the whole pipeline including recognition. The results indicate that there is a convergence to a certain methodology, in terms of layout analysis, with some variations in the approach. However, there is still a considerable need to develop robust methods that deal with the idiosyncrasies of historical books, especially for OCR.

关键词： Layout Optical character recognition software Particle separators Performance evaluation Video recording Text analysis image segmentation

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ICDAR 2013 Competition on Historical Newspaper Layout analysis (HNLA 2013)

ICDAR 2013 Competition on Historical Newspaper Layout Analys...

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International Conference on Document analysis and recognition

This paper presents an objective comparative evaluation of layout analysis methods for scanned historical newspapers. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2013 and the 2nd International Workshop on Historical Document Imaging and Processing (HIP2013), presenting the results of the evaluation of five submitted methods. Two state-of-the-art systems, one commercial and one open-source, are also evaluated for comparison. Two scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and the other evaluating the whole pipeline of segmentation and region classification (with a text extraction goal). The results indicate that there is a convergence to a certain methodology with some variations in the approach. However, there is still a considerable need to develop robust methods that deal with the idiosyncrasies of historical newspapers.

关键词： Layout Particle separators image segmentation Performance evaluation Video recording Text analysis image analysis

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The Significance of Reading Order in Document recognition and Its Evaluation

The Significance of Reading Order in Document Recognition an...

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International Conference on Document analysis and recognition

作者： C. Clausner S. Pletschacher A. Antonacopoulos Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Manchester UK

ISBN: (纸本)9781479901937

Reading order detection and representation is an important task in many digitisation scenarios involving the preservation of the logical structure of a document. The corresponding need for the evaluation of reading order results generated by layout analysis methods poses a particular challenge due to potential deviations between ground truth and actually detected segmentation of the page. To this end a novel evaluation approach that responds to this problem by incorporating region correspondence analysis is proposed. Furthermore, a sophisticated reading order representation scheme is presented and used by the system allowing the grouping of objects with ordered and/or unordered relations. This is a typical requirement for documents with complex layouts such as magazines and newspapers. The evaluation method has been validated using the results of two state-of-the-art OCR / layout analysis systems and a basic top-to-bottom reading order detection algorithm applied on representative samples from the PRImA contemporary and the IMPACT historical document datasets.

关键词： Layout Text analysis Optical character recognition software Performance evaluation Text recognition Engines

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A robust hybrid approach for text line segmentation in historical documents

A robust hybrid approach for text line segmentation in histo...

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International Conference on pattern recognition

作者： Christian Clausner Apostolos Antonacopoulos Stefan Pletschacher Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Greater Manchester UK

Large-scale digitisation of historical documents demands robust methods that cope with the presence of frequent distortions and noisy artefacts. This paper presents a hybrid text line segmentation method that uses a novel data structure and a rule base to combine the strengths of top-down and bottom-up approaches while minimising their weaknesses. The effectiveness of the proposed approach has been methodically evaluated in the context of large-scale digitisation using a standardised framework. Results on a diverse dataset show improved performance over top-down and bottom-up approaches as well as over a leading commercially available system.

关键词： Robustness image segmentation Data structures Libraries Merging Noise Layout

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Handwritten and Machine Printed Text Separation in Document images Using the Bag of Visual Words Paradigm

Handwritten and Machine Printed Text Separation in Document ...

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International Workshop on Frontiers in Handwriting recognition

作者： Konstantinos Zagoris Ioannis Pratikakis Apostolos Antonacopoulos Basilis Gatos Nikos Papamarkos Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Greater Manchester UK Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Athens Greece

In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.

关键词： Visualization Noise image segmentation Hidden Markov models Gabor filters Handwriting recognition Text recognition

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