检索结果-南通市图书馆

Graph convolutional network combined with random walks and graph attention network for node classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

The Journal of China Universities of Posts and Telecommunications 2024年第3期 1-14页

作者： Chen Yong Xie Xiaozhu Weng Wei College of Computer and Information Engineering Xiamen University of Technology Fujian Key laboratory of pattern recognition and image Understanding Xiamen University of Technology

Graph conjoint attention(CAT) network is one of the best graph convolutional networks(GCNs) frameworks, which uses a weighting mechanism to identify important neighbor nodes. However, this weighting mechanism is learned based on static information, which means it is susceptible to noisy nodes and edges, resulting in significant limitations. In this paper, a method is proposed to obtain context dynamically based on random walk, which allows the context-based weighting mechanism to better avoid noise interference. Furthermore, the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT) network to form a model based on a mixed weighting mechanism. The model is named as the context-based and content-based graph convolutional network(CCGCN). CCGCN can better discover important neighbors, eliminate noise edges, and learn node embedding by message passing. Experiments show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.

关键词：

Boosting Multi-modal Ocular recognition via Spatial Feature Reconstruction and Unsupervised image Quality Estimation

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Machine Intelligence Research 2024年第1期21卷 197-214页

作者： Zihui Yan Yunlong Wang Kunbo Zhang Zhenan Sun Lingxiao He Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China JD AI Research Beijing 100176China

In the daily application of an iris-recognition-at-a-distance(IAAD)system,many ocular images of low quality are acquired.As the iris part of these images is often not qualified for the recognition requirements,the more accessible periocular regions are a good complement for recognition.To further boost the performance of IAAD systems,a novel end-to-end framework for multi-modal ocular recognition is proposed.The proposed framework mainly consists of iris/periocular feature extraction and matching,unsupervised iris quality assessment,and a score-level adaptive weighted fusion strategy.First,ocular feature reconstruction(OFR)is proposed to sparsely reconstruct each probe image by high-quality gallery images based on proper feature maps.Next,a brand new unsupervised iris quality assessment method based on random multiscale embedding robustness is proposed.Different from the existing iris quality assess-ment methods,the quality of an iris image is measured by its robustness in the embedding space.At last,the fusion strategy exploits the iris quality score as the fusion weight to coalesce the complementary information from the iris and periocular regions.Extensive experi-mental results on ocular datasets prove that the proposed method is obviously better than unimodal biometrics,and the fusion strategy can significantly improve therecognition performance.

关键词： Iris recognition periocular recognition spatial feature reconstruction fully convolutional network flexible matching unsupervised iris quality assessment adaptive weight fusion

A Novel Multi-Stream Fusion Network for Underwater image Enhancement

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Communications 2024年第2期21卷 166-182页

作者： Guijin Tang Lian Duan Haitao Zhao Feng Liu Jiangsu Key laboratory of image processing and image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Key laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

关键词： image enhancement multi-stream fusion underwater image

Controllable transitions among phase-matching conditions in a single nonlinear crystal

在线全文

学校读者我要写书评

暂无评论

Chinese Optics Letters 2024年第2期22卷 141-147页

作者：曾梓琦尤世欣杨子祥袁晨智由成龙金锐博 Hubei Key laboratory of Optical Information and pattern recognition Wuhan Institute of TechnologyWuhan 430205China Quantum Photonics laboratory Department of Physics and AstronomyLouisiana State UniversityBaton RougeLouisiana 70803USA

Entangled photon pairs are crucial resources for quantum information processing protocols.Via the process of spontaneous parametric downconversion(SPDC),we can generate these photon pairs using bulk nonlinear crystals.Traditionally,the crystal is designed to satisfy a specific type of phase-matching condition.Here,we report controllable transitions among different types of phase matching in a single periodically poled potassium titanyl phosphate crystal.By carefully selecting pump conditions,we can satisfy different phase-matching conditions.This allows us to observe first-order Type-II,fifth-order Type-I,third-order Type-0,and fifth-order Type-II SPDCs.The temperature-dependent spectra of our source were also analyzed in detail.Finally,we discussed the possibility of observing more than nine SPDCs in this crystal.Our work not only deepens the understanding of the physics behind phase-matching conditions,but also offers the potential for a highly versatile entangled biphoton source for quantum information research.

关键词： spontaneous parametric downconversion nonlinear crystals phase-matching condition

CASIA-Iris-Africa:A Large-scale African Iris image Database

在线全文

学校读者我要写书评

暂无评论

Machine Intelligence Research 2024年第2期21卷 383-399页

作者： Jawad Muhammad Yunlong Wang Junxing Hu Kunbo Zhang Zhenan Sun School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China

Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes.Research on iris biometrics has progressed tremendously,partly due to publicly available iris databases.Various databases have been available to researchers that address pressing iris biometric challenges such as constraint,mobile,multispectral,synthetics,long-distance,contact lenses,liveness detection,etc.However,these databases mostly contain subjects of Caucasian and Asian docents with very few Africans.Despite many investigative studies on racial bias in face biometrics,very few studies on iris biometrics have been published,mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain.Furthermore,most of these databases contain a relatively small number of subjects and labelled images.This paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation(CASIA)-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans.The database contains 28717 images of 1023 African subjects(2046 iris classes)with age,gender,and ethnicity attributes that can be useful in demographically sensitive studies of Africans.Sets of specific application protocols are incorporated with the database to ensure the database’s variability and scalability.Performance results of some open-source state-of-the-art(SOTA)algorithms on the database are presented,which will serve as baseline performances.The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms.

关键词： African iris recognition racial bias iris image database biometrics iris recognition

RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第5期79卷 2879-2905页

作者： Xiabin Zhang Zhongyi Hu Lei Xiao Hui Huang College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China Key laboratory of Intelligence image processing and Analysis Wenzhou UniversityWenzhou325035China

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

关键词： Alzheimer CNN structural reparameterization multi head self attention computer aided diagnosis

Anisotropic spin transport and photoresponse characteristics detected by tip movement in magnetic single-molecule junction

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Physics B 2024年第4期33卷 640-648页

作者：陈登辉羊志付新宇秦申奥严岩王传奎李宗良邱帅 Shandong Key laboratory of Medical Physics and image processing&Shandong Provincial Engineering and Technical Center of Light Manipulations School of Physics and ElectronicsShandong Normal UniversityJinan 250358China

Orientation-dependent transport properties induced by anisotropic molecules are enticing in single-molecule junctions.Here,using the first-principles method,we theoretically investigate spin transport properties and photoresponse characteristics in trimesic acid magnetic single-molecule junctions with different molecular adsorption orientations and electrode contact sites.The transport calculations indicate that a single-molecule switch and a significant enhancement of spin transport and photoresponse can be achieved when the molecular adsorption orientation changes from planar geometry to upright geometry.The maximum spin polarization of current and photocurrent in upright molecular junctions exceeds 90%.Moreover,as the Ni tip electrode moves,the tunneling magnetoresistance of upright molecular junctions can be increased to 70%.The analysis of the spin-dependent PDOS elucidates that the spinterfaces between organic molecule and ferromagnetic electrodes are modulated by molecular adsorption orientation,where the molecule in upright molecular junctions yields higher spin polarization.Our theoretical work paves the way for designing spintronic devices and optoelectronic devices with anisotropic functionality base on anisotropic molecules.

关键词： molecular spintronics spin polarization tunneling magnetoresistance photocurrent single-molecule junctions

Comprehensive Relation Modelling for image Paragraph Generation

在线全文

学校读者我要写书评

暂无评论

Machine Intelligence Research 2024年第2期21卷 369-382页

作者： Xianglu Zhu Zhang Zhang Wei Wang Zilei Wang Automation Department University of Science and Technology of ChinaHefei 230027China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100864China

image paragraph generation aims to generate a long description composed of multiple sentences,which is different from traditional image captioning containing only one sentence.Most of previous methods are dedicated to extracting rich features from image regions,and ignore modelling the visual relationships.In this paper,we propose a novel method to generate a paragraph by modelling visual relationships comprehensively.First,we parse an image into a scene graph,where each node represents a specific object and each edge denotes the relationship between two objects.Second,we enrich the object features by implicitly encoding visual relationships through a graph convolutional network(GCN).We further explore high-order relations between different relation features using another graph convolutional network.In addition,we obtain the linguistic features by projecting the predicted object labels and their relationships into a semantic embedding space.With these features,we present an attention-based topic generation network to select relevant features and produce a set of topic vectors,which are then utilized to generate multiple sentences.We evaluate the proposed method on the Stanford image-paragraph dataset which is currently the only available dataset for image paragraph generation,and our method achieves competitive performance in comparison with other state-of-the-art(SOTA)methods.

关键词： image paragraph generation visual relationship scene graph graph convolutional network(GCN) long short-term memory

Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Defence Technology（防务技术） 2024年第1期31卷 496-509页

作者： Chen Wang Zhaohui Shi Minqiang Gu Weicheng Luo Xiaomin Zhu Zhun Fan Shantou University ShantouGuangdongChina National University of Defense Technology ChangshaChina Key Lab of Digital Signal and image processing of Guangdong Province China

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved Vicsek-model.Using the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and obstacles.Next,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its environment.The robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or obstacles.The proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping them.This study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex environments.Swarm robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple targets.Simulation experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

关键词： Swarm intelligence Revolutionary entrapment Flocking Robots Gene regulatory network Vicsek-model Entrapping multiple targets