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On efficient and robust metrics for RANSAC hypotheses and 3D rigid registration

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arXiv 2020年

作者： Yang, Jiaqi Huang, Zhiqiang Quan, Siwen Zhang, Qian Zhang, Yanning Cao, Zhiguo National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University Xi’an710129 China School of Electronic and Control Engineering Chang’an University Xi’an710064 China School of Resources and Environment Hubei University Wuhan430062 China National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

This paper focuses on developing efficient and robust evaluation metrics for RANSAC hypotheses to achieve accurate 3D rigid registration. Estimating six-degree-of-freedom (6-DoF) pose from feature correspondences remains a popular approach to 3D rigid registration, where random sample consensus (RANSAC) is a de-facto choice to this problem. However, existing metrics for RANSAC hypotheses are either time-consuming or sensitive to common nuisances, parameter variations, and different application scenarios, resulting in performance deterioration in overall registration accuracy and speed. We alleviate this problem by first analyzing the contributions of inliers and outliers, and then proposing several efficient and robust metrics with different designing motivations for RANSAC hypotheses. Comparative experiments on four standard datasets with different nuisances and application scenarios verify that the proposed metrics can significantly improve the registration performance and are more robust than several state-of-the-art competitors, making them good gifts to practical applications. This work also draws an interesting conclusion, i.e., not all inliers are equal while all outliers should be equal, which may shed new light on this research problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Deterioration

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

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Local and global structure-aware entropy regularized mean te...

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

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition

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arXiv 2022年

作者： Qin, Rong Huangfu, Luwen Hood, Devon Ma, James Huang, Sheng School of Big Data & Software Engineering Chongqing University Chongqing400044 China Fowler College of Business San Diego State University San DiegoCA92182 United States Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA92182 United States College of Science San Diego State University San DiegoCA92182 United States Department of Business Analysis University of Colorado Colorado SpringsCO80918 United States Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing400044 China

Pavement Distress Recognition (PDR) is an important step in pavement inspection and can be powered by image-based automation to expedite the process and reduce labor costs. Pavement images are often in high-resolution with a low ratio of distressed to non-distressed areas. Advanced approaches leverage these properties via dividing images into patches and explore discriminative features in the scale space. However, these approaches usually suffer from information loss during image resizing and low efficiency due to complex learning frameworks. In this paper, we propose a novel and efficient method for PDR. A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing, and can be flexibly plugged into the image classification network as a pre-network to exploit resolution and scale information. In KIPRN, pyramidal convolution and kernel inversed convolution are specifically designed to mine discriminative information across different feature granularities and scales. The mined information is passed along to the resized images to yield an informative image pyramid to assist the image classification network for PDR. We applied our method to three well-known Convolutional Neural Networks (CNNs), and conducted an evaluation on a large-scale pavement image dataset named CQU-BPDD. Extensive results demonstrate that KIPRN can generally improve the pavement distress recognition of these CNN models and show that the simple combination of KIPRN and EfficientNetB3 significantly outperforms the state-of-the-art patch-based method in both performance and efficiency. © 2022, CC BY-NC-SA.

关键词： Convolution

Text Cover Steganography Using Font Color of the Invisible Characters and Optimized Primary Color-intensities

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Text Cover Steganography Using Font Color of the Invisible C...

International Conference on Communication Technology (ICCT)

作者： Feno Heriniaina Rabevohitra Yantao Li School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China

Text cover steganography is concerned with using text support for hiding information. Despite its long and old history, the art of cover writing is still attracting the attention of researchers in the domain of information hiding. Unfortunately, only a few related works exist in terms of text-covered writing. We present a visual attack on the previous related work. Then, we use invisible characters to hide secret data within the text support. We further use a combination and variation of the three primary colors intensities to vehiculate the message when painting non-printing characters. The proposed algorithm makes use of the low-value color spaces to decrease the color variations of the embedded secret message. It also further strengthens the hiddenness of the non-printing characters even when replaced by visible characters.

关键词： Color Image color analysis Painting software Text processing Visualization Writing

A Lightweight Experimental Platform for Big data Based on Docker Containers

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A Lightweight Experimental Platform for Big Data Based on Do...

作者： Gu Ruijun Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology School of Computer Engineering Jinling Institute of Technology

In recent years, many colleges and universities have set up the major of big *** biggest problem in teaching is that there is no supporting basic experimental environment,and it is difficult to deploy and configure the big data environment at the same time. In addition, the lack of experimental data, experimental teaching plans and experimental manuals in the experimental process makes it difficult to carry out relevant teaching. In order to reduce the cost of laboratory construction and the difficulty of learning big data, a lightweight big data experimental platform was constructed based on virtualized container technology. Through this platform, we can create a big data cluster, provide various suitable experimental environments,focus on the technology itself, and greatly improve the learning efficiency.

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Substrate concentration effect on gene expression in genetic circuits with additional positive feedback

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science China(Technological sciences) 2018年第8期61卷 1175-1183页

作者： WU LuLu WANG Pei Lü JinHu Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Mathematics and Statistics Henan University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University

In gene regulatory networks, gene regulation loops often occur with multiple positive feedback, multiple negative feedback and coupled positive and negative feedback forms. In above gene regulation loops, auto-activation loops are ubiquitous regulatory motifs. This paper aims to investigate a two-component dual-positive feedback genetic circuit, which consists of a double negative feedback circuit and an additional positive feedback loop(APFL). We study effect of substrate concentration on gene expression in the single and the networked systems with APFLs, respectively. We find that substrate concentration can tune stochastic switch behavior in the signal system and then we explore relationship of substrate concentration with positive feedback strength in aspect of stochastic switch behavior. Furthermore, we also discuss gene expression and stochastic switch behavior in the networked systems with APFLs. Based on analysis in the networked systems, we discover that genes express in some specific cells and do not express in the other cells when the expression achieves its steady state. These results can be used to well explain the character of regionalization in the expression of genes and the phenomenon of gene differentiation.

关键词： substrate concentration additional positive feedback loop stochastic switch behavior gene expression gene differ-entiation

Forward engineering completeness for software by using requirements validation framework 31

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Forward engineering completeness for software by using requi...

31st International Conference on software engineering and Knowledge engineering, SEKE 2019

作者： Iqbal, Nayyar Sang, Jun Gao, Min Hu, Haibo Xiang, Hong Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing400044 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Computer Science University of Agriculture Faisalabad Faisalabad38000 Pakistan

ISBN: (纸本)1891706489

In software development environment, software companies usually ignore the user requirements validation process in requirement gathering phase, which results in large number of modifications being required in the software maintenance phase to fulfill the customer requirements. Identification of accurate requirements from user stories and determining the effectiveness of work deliverable of software industry has always been a challenging task. In this paper, a new measurement approach for forward engineering completeness for software was introduced by using requirements validation framework. The forward engineering completeness for software was measured in two steps. In the first step, software component structure was developed in order to find the functional and nonfunctional requirements rejected by the customers in the requirement validation framework. In the second step, completeness of software from component-based development was determined in which the following parameters, such as functional, non-functional completeness attributes, were considered in the measurement process, and the unadopted attributes of the reuse code were also considered. Quality level for the attributes were assigned based upon the valuation of interior quality of the source code. Therefore, it resulted in the reduction of development time required for the software and the cost required for the software development was also reduced. A case study was incorporated in this research to explain the measurement process of forward engineering completeness. If the forward engineering code is satisfying the quality standards, then the code is in the completeness form. The attributes of code that negates to be used were considered as unadopted attributes. © 2019 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Computer software reusability

Tensor completion via convolutional sparse coding regularization

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arXiv 2020年

作者： Wu, Zhebin Liao, Tianchi Chen, Chuan Liu, Cong Zheng, Zibin Zhang, Xiongjun The School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China National Engineering Research Center of Digital Life Sun Yat-Sen University Guangzhou China The School of Software Engineering Sun Yat-Sen University Zhuhai519082 China The School of Mathematics and Statistics Hubei Key Laboratory of Mathematical Sciences Central China Normal University Wuhan430079 China

Tensor data often suffer from missing value problem due to the complex high-dimensional structure while acquiring them. To complete the missing information, lots of Low-Rank Tensor Completion (LRTC) methods have been proposed, most of which depend on the low-rank property of tensor data. In this way, the low-rank component of the original data could be recovered roughly. However, the shortcoming is that the detail information can not be fully restored, no matter the Sum of the Nuclear Norm (SNN) nor the Tensor Nuclear Norm (TNN) based methods. On the contrary, in the field of signal processing, Convolutional Sparse Coding (CSC) can provide a good representation of the high-frequency component of the image, which is generally associated with the detail component of the data. Nevertheless, CSC can not handle the low-frequency component well. To this end, we propose two novel methods, LRTC-CSC-I and LRTC-CSC-II, which adopt CSC as a supplementary regularization for LRTC to capture the high-frequency components. Therefore, the LRTC-CSC methods can not only solve the missing value problem but also recover the details. Moreover, the regularizer CSC can be trained with small samples due to the sparsity characteristic. Extensive experiments show the effectiveness of LRTC-CSC methods, and quantitative evaluation indicates that the performance of our models are superior to state-of-the-art methods. © 2020, CC BY.

关键词： Convolution