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arXiv 2020年

作者： Liu, Ajian Li, Xuan Wan, Jun Liang, Yanyan Escalera, Sergio Escalante, Hugo Jair Madadi, Meysam Jin, Yi Wu, Zhuoyuan Yu, Xiaogang Tan, Zichang Yuan, Qi Yang, Ruikun Zhou, Benjia Guo, Guodong Li, Stan Z. Faculty of Information Technology Avenida WaiLong Taipa Macau China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China Universitat de Barcelona and Computer Vision Center Barcelona Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico School of Software Beihang University Beijing China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Beijing Westlake University Hangzhou China

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1;607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face *** organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

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Sclera Segmentation Benchmarking Competition in Cross-resolution Environment

Sclera Segmentation Benchmarking Competition in Cross-resolu...

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IAPR International Conference on Biometrics (ICB)

作者： Abhijit Das Umapada Pal Michael Blumenstein Caiyong Wang Yong He Yuhao Zhu Zhenan Sun Inria Sophia Antipolis – Méditerranée France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Software University of Technology Sydney Australia Institute of Automation Chinese Academy of Sciences(CASIA)

This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using .a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed. Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.

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Proceedings of 2nd International Conference on computer vision & Image Processing 1

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丛书名： Advances in Intelligent Systems and Computing

2018年

作者： Bidyut B. Chaudhuri Mohan S. Kankanhalli Balasubramanian Raman

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Image synthesis with adversarial networks: A comprehensive survey and case studies

arXiv

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arXiv 2020年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Granger, Eric Zhou, Huiyu Wang, Ruili Emre Celebi, M. Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Laboratoire d’imagerie De vision et d’intelligence artificielle École de technologie supérieure Montreal Canada School of Informatics University of Leicester United Kingdom School of Natural and Computational Sciences Massey University Auckland New Zealand Department of Computer Science University of Central Arkansas United States

Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis. We summarize the synthetic image generation methods, and discuss the categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation. We organize the literature based on their base models, developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. We present milestones of adversarial models, review an extensive selection of previous works in various categories, and present insights on the development route from the model-based to data-driven methods. Further, we highlight a range of potential future research directions. One of the unique features of this review is that all software implementations of these GAN methods and datasets have been collected and made available in one place at https://***/pshams55/GAN-CaseStudy. © 2020, CC BY-NC-ND.

关键词： Generative adversarial networks

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Inter-Species Cell Detection: Datasets on pulmonary hemosiderophages in equine, human and feline specimens

arXiv

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arXiv 2021年

作者： Marzahl, Christian Hill, Jenny Stayt, Jason Bienzle, Dorothee Welker, Lutz Wilm, Frauke Voigt, Jörn Aubreville, Marc Maier, Andreas Klopfleisch, Robert Breininger, Katharina Bertram, Christof A. Pattern Recognition Lab Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Research and Development EUROIMMUN Medizinische Labordiagnostika AG Lübeck Germany VetPath Laboratory Services AscotWA Australia Department of Pathobiology OntarioVeterinary College University of Guelph GuelphON Canada Cytology Laboratory Lungen Clinic Grosshansdorf Germany Technische Hochschule Ingolstadt Ingolstadt Germany Institute of Veterinary Pathology Freie Universität Berlin Germany Department of Artifical Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Institute of Pathology University of Veterinary Medicine Vienna Austria

Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologists. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered. Copyright © 2021, The Authors. All rights reserved.

关键词： Data visualization

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Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations

European Journal of Radiology Artificial Intelligence

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European Journal of Radiology Artificial Intelligence 2025年 3卷

作者： Mahshad Lotfinia Arash Tayebiarasteh Samaneh Samiei Mehdi Joodaki Soroosh Tayebi Arasteh Department of Diagnostic and Interventional Radiology University Hospital RWTH Aachen Aachen Germany Department of Computer Engineering Hamedan University of Technology Hamedan Iran Quantitative Cell Dynamics and Translational Systems Biology University Hospital RWTH Aachen Aachen Germany Institute for Computational Genomics Joint Research Center for Computational Biomedicine University Hospital RWTH Aachen Aachen Germany Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Urology Stanford University Stanford CA USA Department of Radiology Stanford University Stanford CA USA

Background Reliable artificial intelligence (AI) models for medical image analysis often depend on large and diverse labeled datasets. Federated learning (FL) offers a decentralized and privacy-preserving approach to training but struggles in highly non-independent and identically distributed (non-IID) settings, where institutions with more representative data may experience degraded performance. Moreover, existing large-scale FL studies have been limited to adult datasets, neglecting the unique challenges posed by pediatric data, which introduces additional non-IID variability. Methods To address these limitations, we analyzed n = 398,523 adult chest radiographs from diverse institutions across multiple countries and n = 9125 pediatric images, leveraging transfer learning from general-purpose self-supervised image representations to classify pneumonia and cases with no abnormality. Results Using state-of-the-art vision transformers, we found that FL improved performance only for smaller adult datasets (P < 0.001) but degraded performance for larger datasets (P ≤ 0.063) and pediatric cases (P = 0.242). However, equipping FL with self-supervised weights significantly enhanced outcomes across pediatric cases (P = 0.031) and most adult datasets (P ≤ 0.007), except the largest dataset (P = 0.052). Conclusions These findings underscore the potential of easily deployable general-purpose self-supervised image representations to address non-IID challenges in clinical FL applications and highlight their promise for enhancing patient outcomes and advancing pediatric healthcare, where data scarcity and variability remain persistent obstacles.

关键词： Chest Radiography Federated Learning Self-Supervised Learning Pediatrics DINOv2

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Symmetry features for license plate classification

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CAAI Transactions on Intelligence Technology 2018年第3期3卷 176-183页

作者： Karpuravalli Srinivas Raghunandan Palaiahnakote Shivakumara Lolika Padmanabhan Govindaraju Hemantha Kumar Tong Lu Umapada Pal Department of Studies in Computer Science University of Mysore Karnataka India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia PES Institute of Technology Bangalore Karnataka India National Key Lab for Novel Software Technology Nanjing University Nanjing People's Republic of China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

Achieving high recognition rate for license plate images is challenging due to multi-type images. We present new symmetry features based on stroke width for classifying each input license image as private, taxi, cursive text, when they expand the symbols by writing and non-text such that an appropriate optical character recognition （OCR） can be chosen for enhancing recognition performance. The proposed method explores gradient vector flow （GVF） for defining symmetry features, namely, GVF opposite direction, stroke width distance, and stroke pixel direction. Stroke pixels in Canny and Sobel which satisfy the above symmetry features are called local candidate stroke pixels. Common stroke pixels of the local candidate stroke pixels are considered as the global candidate stroke pixels. Spatial distribution of stroke pixels in local and global symmetry are explored by generating a weighted proximity matrix to extract statistical features, namely, mean, standard deviation, median and standard deviation with respect the median. The feature matrix is finally fed to an support vector machine （SVM） classifier for classification. Experimental results on large datasets for classification show that the proposed method outperforms the existing methods. The usefulness and effectiveness of the proposed classification is demonstrated by conducting recognition experiments before and after classification.

关键词：车牌图像图像识别识别技术计算机技术

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diffGrad: An optimization method for convolutional neural networks

arXiv

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arXiv 2019年

作者： Dubey, Shiv Ram Chakraborty, Soumendu Roy, Swalpa Kumar Mukherjee, Snehasis Singh, Satish Kumar Chaudhuri, Bidyut Baran The Computer Vision Group Indian Institute of Information Technology Sri City Andhra Pradesh Chittoor517646 India The Indian Institute of Information Technology Uttar Pradesh Lucknow India The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India Techno India University Sector V Salt Lake City Kolkata700091 India The Computer Vision and Biometrics Laboratory Indian Institute of Information Technology Allahabad211015 India

Stochastic Gradient Decent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which a function has the steepest rate of change. The main problem with basic SGD is to change by equal sized steps for all parameters, irrespective of gradient behavior. Hence, an efficient way of deep network optimization is to make adaptive step sizes for each parameter. Recently, several attempts have been made to improve gradient descent methods such as AdaGrad, AdaDelta, RMSProp and Adam. These methods rely on the square roots of exponential moving averages of squared past gradients. Thus, these methods do not take advantage of local change in gradients. In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i.e., diffGrad). In the proposed diffGrad optimization technique, the step size is adjusted for each parameter in such a way that it should have a larger step size for faster gradient changing parameters and a lower step size for lower gradient changing parameters. The convergence analysis is done using the regret bound approach of online learning framework. Rigorous analysis is made in this paper over three synthetic complex non-convex functions. The image categorization experiments are also conducted over the CIFAR10 and CIFAR100 datasets to observe the performance of diffGrad with respect to the state-of-the-art optimizers such as SGDM, AdaGrad, AdaDelta, RMSProp, AMSGrad, and Adam. The residual unit (ResNet) based Convolutional Neural Networks (CNN) architecture is used in the experiments. The experiments show that diffGrad outperforms other optimizers. Also, we show that diffGrad performs uniformly well for training CNN using different activation functions. The source code is made publicly available at https://***/shivram1987/diffGrad. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

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Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

arXiv

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arXiv 2021年

作者： Pan, Liang Wu, Tong Cai, Zhongang Liu, Ziwei Yu, Xumin Rao, Yongming Lu, Jiwen Zhou, Jie Xu, Mingye Luo, Xiaoyuan Fu, Kexue Gao, Peng Wang, Manning Wang, Yali Qiao, Yu Zhou, Junsheng Wen, Xin Xiang, Peng Liu, Yu-Shen Han, Zhizhong Yan, Yuanjie An, Junyi Zhu, Lifa Lin, Changwei Liu, Dongrui Li, Xin Gómez-Fernández, Francisco Wang, Qinlong Yang, Yang S-Lab Nanyang Technological University Singapore SenseTime-CUHK Joint Lab The Chinese University of Hong Kong Hong Kong Sensetime Research Shanghai AI Laboratory China Department of Automation Tsinghua University China University of Chinese Academy of Sciences China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Digital Medical Research Center School of Basic Medical Science Fudan University China School of Software BNRist Tsinghua University China *** Wayne State University State Key Laboratory for Novel Software Technology Nanjing University China DeepGlint Shanghai Jiao Tong University China Sichuan University China Xi'an Jiaotong University China

As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem for computer vision. With a single incomplete point cloud, it becomes the partial point cloud completion problem. Given multiple different observations, 3D reconstruction can be addressed by performing partial-to-partial point cloud registration. Recently, a large-scale Multi-View Partial (MVP) point cloud dataset has been released, which consists of over 100,000 high-quality virtual-scanned partial point clouds. Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration. In total, 128 participants registered for the competition, and 31 teams made valid submissions. The top-ranked solutions will be analyzed, and then we will discuss future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Surface measurement

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Blind Super-Resolution With Iterative Kernel Correction

Blind Super-Resolution With Iterative Kernel Correction

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IEEE/CVF Conference on computer vision and pattern recognition

作者： Jinjin Gu Hannan Lu Wangmeng Zuo Chao Dong The School of Science and Engineering The Chinese University of Hong Kong School of Computer Science and Technology Harbin Institute of Technology ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781728132945

Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downs ampling is predefined/known (e.g., bicubic). However, the blur kernels involved in real applications are complicated and unknown, resulting in severe performance drop for the advanced SR methods. In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown. We draw the observation that kernel mismatch could bring regular artifacts (either over-sharpening or over-smoothing), which can be applied to correct inaccurate blur kernels. Thus we introduce an iterative correction scheme - IKC that achieves better results than direct kernel estimation. We further propose an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur-kernels, named SFTMD. Extensive experiments on synthetic and real-world images show that the proposed IKC method with SFTMD can provide visually favorable SR results and the state-of-the-art performance in blind SR problem.

关键词： Seeds Kernel SAVANNAH RIVER OPERATIONS OFFICE Strontium super-resolution Blind Iterative

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