检索结果-内蒙古大学图书馆

A novel visual attention method for target detection from SAR images

Chinese Journal of Aeronautics 2019年第8期32卷 1946-1958页

作者： Fei GAO Aidong LIU Kai LIU Erfu YANG Amir HUSSAIN School of Electronic and Information Engineering Beihang UniversityBeijing 100083China Space Mechatronic Systems Technology Laboratory Department of DesignManufacture and Engineering ManagementUniversity of StrathclydeGlasgow G1 1XJUK Cognitive Big Data and Cyber-Informatics(CogBID)Laboratory School of ComputingEdinburgh Napier UniversityEdinburgh EH10 5DTScotlandUK

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down *** the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.

关键词： Learning strategy Synthetic Aperture Radar(SAR) images Target detection Top-down Visual attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Hadamard matrix guided online hashing

arXiv

引用

arXiv 2019年

作者： Lin, Mingbao Ji, Rongrong Liu, Hong Sun, Xiaoshuai Chen, Shen Tian, Qi Media Analytics and Computing Laboratory Department of Cognitive Science School of Informatics Xiamen University China Peng Cheng Laboratory Shenzhen China Huawei Noah's Art Lab China

Online image hashing has attracted increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly. Its key challenge lies in the difficulty of balancing the learning timeliness and model accuracy. To this end, most works follow a supervised setting, i.e., using class labels to boost the hashing performance, which defects in two aspects: First, strong constraints, e.g., orthogonal or similarity preserving, are used, which however are typically relaxed and lead to large accuracy drop. Second, large amounts of training batches are required to learn the up-to-date hash functions, which largely increase the learning complexity. To handle the above challenges, a novel supervised online hashing scheme termed Hadamard Matrix Guided Online Hashing (HMOH) is proposed in this paper. Our key innovation lies in introducing Hadamard matrix, which is an orthogonal binary matrix built via Sylvester method. In particular, to release the need of strong constraints, we regard each column of Hadamard matrix as the target code for each class label, which by nature satisfies several desired properties of hashing codes. To accelerate the online training, LSH is first adopted to align the lengths of target code and to-be-learned binary code. We then treat the learning of hash functions as a set of binary classification problems to fit the assigned target code. Finally, extensive experiments on four widely-used datasets demonstrate the superior accuracy and efficiency of the proposed method over various state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Binary codes

来源：评论

学校读者我要写书评

暂无评论

Hadamard codebook based deep hashing

arXiv

引用

arXiv 2019年

作者： Chen, Shen Cao, Liujuan Lin, Mingbao Wang, Yan Sun, Xiaoshuai Wu, Chenglin Qiu, Jingfei Ji, Rongrong Fujian Laboratory of Sensing and Computing for Smart City Department Cognitive Science School of Informatics Xiamen University China Pinterest San Francisco United States Fuzhi Xiamen China Peng Cheng Laboratory Shenzhen China

As an approximate nearest neighbor search technique, hashing has been widely applied in large-scale image retrieval due to its excellent efficiency. Most supervised deep hashing methods have similar loss designs with embedding learning, while quantizing the continuous high-dim feature into compact binary space. We argue that the existing deep hashing schemes are defective in two issues that seriously affect the performance, i.e., bit independence and bit balance. The former refers to hash codes of different classes should be independent of each other, while the latter means each bit should have a balanced distribution of +1s and −1s. In this paper, we propose a novel supervised deep hashing method, termed Hadamard Codebook based Deep Hashing (HCDH), which solves the above two problems in a unified formulation. Specifically, we utilize an off-the-shelf algorithm to generate a binary Hadamard codebook to satisfy the requirement of bit independence and bit balance, which subsequently serves as the desired outputs of the hash functions learning. We also introduce a projection matrix to solve the inconsistency between the order of Hadamard matrix and the number of classes. Besides, the proposed HCDH further exploits the supervised labels by constructing a classifier on top of the outputs of hash functions. Extensive experiments demonstrate that HCDH can yield discriminative and balanced binary codes, which well outperforms many state-of-the-arts on three widely-used benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources

arXiv

引用

arXiv 2024年

作者： Hansen, Lasse Hyldig Andersen, Nikolaj Gallifant, Jack McCoy, Liam G. Stone, James K. Izath, Nura Aguirre-Jerez, Marcela Bitterman, Danielle S. Gichoya, Judy Celi, Leo Anthony Cognitive Science Aarhus University Jens Chr. Skou 2 Aarhus8000 Denmark Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Department of Critical Care Guy’s & St Thomas’ NHS Trust London United Kingdom Division of Neurology University of Alberta AB Canada University of Manitoba Max Rady College of Medicine WinnipegMB Canada Faculty of Computing Mbarara University of Science and Technology Mbarara Uganda Digital Health Department Fundacion Arturo Lopez Perez Santiago Chile Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women’s Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Radiology Emory University School of Medicine GA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States

Background Advancements in Large Language Models (LLMs) hold transformative potential in healthcare, however, recent work has raised concern about the tendency of these models to produce outputs that display racial or gender biases. Although training data is a likely source of such biases, exploration of disease and demographic associations in text data at scale has been limited. Methods We conducted a large-scale textual analysis using a dataset comprising diverse web sources, including Arxiv, Wikipedia, and Common Crawl. The study analyzed the context in which various diseases are discussed alongside markers of race and gender. Given that LLMs are pre-trained on similar datasets, this approach allowed us to examine the potential biases that LLMs may learn and internalize. We compared these findings with actual demographic disease prevalence as well as GPT-4 outputs in order to evaluate the extent of bias representation. Results Our findings indicate that demographic terms are disproportionately associated with specific disease concepts in online texts. gender terms are prominently associated with disease concepts, while racial terms are much less frequently associated. We find widespread disparities in the associations of specific racial and gender terms with the 18 diseases analyzed. Most prominently, we see an overall significant overrepresentation of Black race mentions in comparison to population proportions. Conclusions Our results highlight the need for critical examination and transparent reporting of biases in LLM pretraining datasets. Our study suggests the need to develop mitigation strategies to counteract the influence of biased training data in LLMs, particularly in sensitive domains such as healthcare. © 2024, CC BY.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Back-propagation Artificial Neural Network for Early Diabetic Retinopathy Detection Based On A Priori Knowledge

Back-propagation Artificial Neural Network for Early Diabeti...

引用

作者： Huiqun Wu Shenghui Zhao Xiaofeng Zhang Aimin Sang Jiancheng Dong Kui Jiang Department of Medical Informatics Medical School of Nantong University Nantong University The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions (Wuyi University) Department of Ophthalmology Affiliated Hospital of Nantong University

Purpose: We aim to develop a back-propagation artificial neural network（BPANN） improved by a priori knowledge and to compare its efficacy with other methods in early diabetic retinopathy（DR） detection. Methods: A total of 240 fundus images, composed of 120 early-stage DR and 120 normal images, were obtained with the same 45° field of view camera, with the macula at the center, as a cohort for further training. All retinal images were processed, and a priori knowledge features such as blood vessel width and tortuosity were semi-automatically extracted. An improved BP-ANN with a priori knowledge was developed, and its efficacy was compared with that of the traditional BP network and SVM. Besides, k-fold cross validation method was conducted to demonstrate the efficiency of the proposed methods. We also developed a graphical user interface of our proposed BP-ANN to aid in DR screening. Results: Our 10 randomization and 5-fold cross validation results of SVM, traditional BP, and improved BP were compared. The results indicated that the BP-ANN with a priori knowledge can achieve better detection results. Besides, our results were also comparable with other reported state-of-art algorithms. During the training stage, the epoch in the improved BP-ANN was less than that in the traditional BP group（109 vs 254）, indicating that the time cost was shorter when using our improved BP-ANN. Furthermore, the accuracy and epoch of both the traditional BP and our improved BP network obtained better performances when the number of hidden neurons was 20. Conclusions: A priori knowledge-based BP-ANN could be a promising measure for early DR detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Developing knowledge-based systems using data mining techniques for advising secondary school students in field of interest selection 23rd

Developing knowledge-based systems using data mining techniq...

引用

23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018

作者： Yitagesu, Sofonias Feng, Zhiyong Meshesha, Million Mekuria, Getachew Yasin, Muhammad Qasim School of Computer Software Tianjin University Tianjin300350 China Tianjins Key Laboratory of Cognitive Computing and Application Tianjin300350 China School of Informatics Addis Ababa University Addis Ababa Ethiopia College of Computing Debre Berhan University Debre Berhan Ethiopia

ISBN: (纸本)9783319914541

Ethiopia gives a highly emphasis on a secondary school and reform program of impressive expansion. In doing this, students interest towards the fields they are assigned to needs to be taken into consideration. This has been put into practice when the Ministry of Education and preparatory schools have assigned students in fields of studies based on their performance at secondary schools. However, they used only the students grade 10 Ethiopian General School Leaving Certificate Examination result to assign them. The objective of this study is to develop a knowledge-based systems using machine learning (data mining) techniques that consults the students in their field of study selection process. In this study, the hybrid model that was developed for academic research is used. To build the predictive model, 9364 sample students data from selected secondary schools are used. The sample data is preprocessed for missing values, outliers, noisy and errors. Then the model is experimented using decision tree (j48) and rule induction (PART) algorithms. In this study as compared to j48, the PART unpruned decision list algorithm has 98.003% predictive performance. Thus, the knowledge discovered with this algorithm is further used to build the knowledge-based systems. Hence, the Java program is used to integrate data mining results to knowledge-based systems. As a result, the developed knowledge based-systems is used to predict students field of study based on their performance at secondary school. The study concludes that, to build the accurate knowledge-based systems discovering knowledge using data mining techniques is significant. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Knowledge based systems

来源：评论

学校读者我要写书评

暂无评论

Learning to reconstruct and understand indoor scenes from sparse views

arXiv

引用

arXiv 2019年

作者： Yang, Jingyu Xu, Ji Li, Kun Lai, Yu-Kun Yue, Huanjing Lu, Jianzhi Wu, Hao Liu, Yebin School of Electrical and Information Engineering Tianjin University Tianjin300072 China Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin300350 China School of Computer Science and Informatics Cardiff University Wales United Kingdom 3vjia company Department of Automation Tsinghua University Beijing10084 China

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation of indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g., 3-5) color images from uncalibrated sparse views as input, which greatly simplifies data acquisition and extends applicable scenarios. Since different views have limited overlaps, our method allows a single image as input to discern the depth and semantic information of the scene. The key issue is how to recover relatively accurate depth from single images and reconstruct a 3D scene by fusing very few depth maps. To address this problem, we first design an iterative deep architecture, Iter-Net, that estimates depth and semantic segmentation alternately, so that they benefit each other. To deal with the little overlap and non-rigid transformation between views, we further propose a joint global and local registration method to reconstruct a 3D scene with semantic information from sparse *** also make available a new indoor synthetic dataset simultaneously providing photorealistic high-resolution RGB images, accurate depth maps and pixel-level semantic labels for thousands of complex layouts, useful for training and evaluation. Experimental results on public datasets and our dataset demonstrate that our method achieves more accurate depth estimation, smaller semantic segmentation errors and better 3D reconstruction results, compared with stateof-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

Back-propagation Artificial Neural Network for Early Diabetic Retinopathy Detection Based On A Priori Knowledge

引用

Journal of Physics: Conference Series 2020年第1期1437卷

作者： Wu, Huiqun Zhao, Shenghui Dong, Jiancheng Jiang, Kui Zhang, Xiaofeng Sang, Aimin Department of Medical Informatics Medical School of Nantong University Nantong China Nantong University The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions (Wuyi University) Nantong China Department of Ophthalmology Affiliated Hospital of Nantong University Nantong China

Purpose: We aim to develop a back-propagation artificial neural network (BP-ANN) improved by a priori knowledge and to compare its efficacy with other methods in early diabetic retinopathy (DR) detection. Methods: A total of 240 fundus images, composed of 120 early-stage DR and 120 normal images, were obtained with the same 45° field of view camera, with the macula at the center, as a cohort for further training. All retinal images were processed, and a priori knowledge features such as blood vessel width and tortuosity were semi-automatically extracted. An improved BP-ANN with a priori knowledge was developed, and its efficacy was compared with that of the traditional BP network and SVM. Besides, k-fold cross validation method was conducted to demonstrate the efficiency of the proposed methods. We also developed a graphical user interface of our proposed BP-ANN to aid in DR screening. Results: Our 10 randomization and 5-fold cross validation results of SVM, traditional BP, and improved BP were compared. The results indicated that the BP-ANN with a priori knowledge can achieve better detection results. Besides, our results were also comparable with other reported state-of-art algorithms. During the training stage, the epoch in the improved BP-ANN was less than that in the traditional BP group (109 vs 254), indicating that the time cost was shorter when using our improved BP-ANN. Furthermore, the accuracy and epoch of both the traditional BP and our improved BP network obtained better performances when the number of hidden neurons was 20. Conclusions: A priori knowledge-based BP-ANN could be a promising measure for early DR detection. CCS: Information system→Expert system

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adaptive Noise Cancelation Using Fuzzy Brain Emotional Learning Network

Advances in Intelligent Systems and Computing

引用

Advances in Intelligent Systems and computing 2018年 650卷 171-182页

作者： Zhou, Qianqian Lin, Chih-Min Chao, Fei Fujian Provincial Key Laboratory of the Brain-like Computing Cognitive Science Department School of Informatics Xiamen University XiamenFujian China Department of Electrical Engineering Yuan Ze University Chung-LiTao-Yuan320 Taiwan

ISBN: (纸本)9783319669380

This paper proposes a fuzzy brain emotional learning network for adaptive noise cancelation. The proposed network is based on brain emotional learning algorithm which is developed according to the emotional learning process of mammalian and the fuzzy inference is added for better ability to handle uncertainties. Parameters in the network are modified online by the derived adaption laws. In addition, a stable convergence is guaranteed by utilizing the Lyapunov stability theorem. Finally, in order to demonstrate the performance of the proposed filter, it is applied in a signal processing application where different source signals and noise signals are used. A comparison between the proposed method, Least mean square algorithm and a fuzzy cerebellar model articulation controller filter shows that the proposed method can converge faster even when the source signal is corrupted severely. © 2018, Springer International Publishing AG.

关键词： Fuzzy inference

来源：评论

学校读者我要写书评

暂无评论

An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

引用

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：