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arXiv 2022年

作者： Li, Xingyi Hong, Chaoyi Wang, Yiran Cao, Zhiguo Xian, Ke Lin, Guosheng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of AIA Huazhong University of Science and Technology China S-Lab Nanyang Technological University Singapore

We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fail to recover the fine appearance details, especially in self-occluded areas. This is because a single view only provides limited information. We observe that man-made objects usually exhibit symmetric appearances, which introduce additional prior knowledge. Motivated by this, we investigate the potential performance gains of explicitly embedding symmetry into the scene representation. In this paper, we propose SymmNeRF, a neural radiance field (NeRF) based framework that combines local and global conditioning under the introduction of symmetry priors. In particular, SymmNeRF takes the pixel-aligned image features and the corresponding symmetric features as extra inputs to the NeRF, whose parameters are generated by a hypernetwork. As the parameters are conditioned on the image-encoded latent codes, SymmNeRF is thus scene-independent and can generalize to new scenes. Experiments on synthetic and real-world datasets show that SymmNeRF synthesizes novel views with more details regardless of the pose transformation, and demonstrates good generalization when applied to unseen objects. Code is available at: https://***/xingyi-li/SymmNeRF. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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Extended State Observer Based Fault-Tolerant Predictive Control for a Four-Mecanum Wheels Mobile Robot 14

Extended State Observer Based Fault-Tolerant Predictive Cont...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Yang, Binghao Wang, Dongliang Wen, Ziling Wei, Wu Li, Wenji Fan, Zhun Shantou University Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong 515063 China Shantou University School of Department of Electronic and Information Engineering Guangdong 515063 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Guangzhou510640 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9798331506056

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment. © 2024 IEEE.

关键词： Mobile robots

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Robust Segmentation of Freehand Ultrasound image Slices Using Gradient Vector Flow Fast Geometric Active Contours

Robust Segmentation of Freehand Ultrasound Image Slices Usin...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Honggang Yu M.S. Pattichis M.B. Goens Department of Electrical and Computer Engineering image and video Processing and Communications Lab (ivPCL) Albuquerque NM USA Children's Hospital Heart Center The University of New Mexico Albuquerque NM USA

ISBN: (纸本)1424400694

We propose a new semi-automatic segmentation strategy on echocardiographic images, which combines a recently introduced gradient vector flow (GVF) fast geometric active contour (GAC) model and a modified level sets methods applied to echocardiographic data by Corsi et al.. We call it adaptive GVF GAC model. We note that echocardiographic images are characterized by high levels of speckle noise, weakly-defined boundaries and severe gaps. We show that the new method, adapted for single object segmentation, can provide significantly improved performance over a competing level set method, and that was in turn shown to perform better than the original gradient vector flow method. The new method modifies the advection term in the speed function adoptively by estimating how close the propagated curve is to the target boundaries. We show both synthetic and real, freehand ultrasound image and echocardiographic image examples to illustrate the robustness and accuracy of the new segmentation method

关键词： Robustness image segmentation Ultrasonic imaging Active contours Level set Deformable models image reconstruction Solid modeling Heart Speckle

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Millimeter-wave radar image analysis for the traffic sensing 2

Millimeter-wave radar image analysis for the traffic sensing

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2nd Spring International Conference on Defence Technology, ICDT 2020

作者： Wu, H.M. Qi, F. Wang, J.K. Wang, Y. School of Communication Science and Engineering Northeastern University Liaoning Province Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

Millimeter-wave(MMW) radar sensing is one of the most promising technologies to provide safe navigation for autonomous vehicles due to its expected high-resolution imaging capability However, driverless cars have higher request for different environment and light conditions. Therefore, millimetre-wave imaging is of paramount importance for complex load scenario. In this paper, we have built models of pavement pits and bulges and analysed their with differences ways of antennas. A comparison of the imaging performance of experimental systems operating at a MMW radar and a Lidar is presented with the analysis of features for initial image interpretation Experimental images of the complex road surface are made by a 94GHz frequency-modulated continuous-wave (FMCW) radar technique with 3mm wavelength. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Knowledge Augmented Attention Gating Embedding for Link Prediction

Knowledge Augmented Attention Gating Embedding for Link Pred...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Zewei Chen Shuhong Chen Chen Li Xianwei Zheng Minfan He Xutao Li Department of Electronic Engineering Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China School of Mathematics Foshan University Foshan China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Knowledge graphs (KGs) use graphical representations to illustrate relations between entities, forming a structured knowledge base. However, KGs often suffer from incompleteness, lacking some entities and their interactions. Current link prediction technologies based on KG embeddings primarily focus on learning entity features and relation patterns from existing KGs to address the incompleteness issue. Yet, these methods encounter the inherent sparsity issue in KGs—uneven distribution of entity connections and scarcity of direct links. This complexity challenges models in identifying intricate, potential associations, thereby compromising link prediction accuracy and overall model performance. In this paper, we introduce a innovative Knowledge Augmented Attention-based Gating Embedding (KAAGE) framework for link prediction. Our model augments knowledge by reversing relations and triples, then integrates both original and augmented structural data through gating units. Extensive experiments on three established benchmark datasets demonstrate the superiority of KAAGE over current models. Our implementation code is available at https://***/22zwChen/KAAGE

关键词： Codes Fuses Semantics Knowledge based systems Knowledge graphs Information processing Predictive models Benchmark testing Data models Complexity theory

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Semi-supervised emotional classification of color images by learning from cloud

Semi-supervised emotional classification of color images by ...

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International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Na Li Yong Xia Yuwei Xia Shaanxi Key Lab of Speech & Image Information Processing (SAIIP) Northwestern Polytechnical University Xi'an China Computer Science College Xi'an Polytechnic University Xi'an China

Classification of images based on the feelings generated by each image in its reviewers is becoming more and more popular. Due to the difficulty of gathering training data, this task is intrinsically a small-sample learning problem. Hence, the results produced by most existing solutions are less accurate. In this paper, we propose the semi-supervised hierarchical classification (SSHC) algorithm for emotional classification of color images. We extract three groups of features for each classification task and use those features in a two-level classification model that is based on the support vector machine (SVM) and Adaboost technique. To enlarge the training dataset, we employ each training image to retrieve similar images from the Internet cloud and jointly use the manually labeled small dataset and retrieved large but unlabeled dataset to train a classifier via semi-supervised learning. We have evaluated the proposed algorithm against the fuzzy similarity-based emotional classification (FSBEC) algorithm and another supervised hierarchical classification algorithm that does not learn from online images in three bi-class classification tasks, including “warm vs. cool”, “light vs. heavy” and “static vs. dynamic”. Our pilot results suggest that, by learning from the similar images archived in the Internet cloud, the proposed SSHC algorithm can produce more accurate emotional classification of color images.

关键词： image color analysis Feature extraction Training Support vector machines image edge detection Histograms Color

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A directional-progressive search method for infrared small target detection 3

A directional-progressive search method for infrared small t...

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3rd Optoelectronics Global Conference, OGC 2018

作者： Zhang, Xiangyue Ding, Qinghai Luo, Haibo Hui, Bin Chang, Zheng Zhang, Junchao Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing CAS Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China Space Star Technology Co. Ltd Beijing100086 China

ISBN: (纸本)9781538673973

Infrared small target detection plays a key role in infrared precision guidance and infrared early-warning system. It has been a difficult problem for researchers to study on how to detect targets accurately at a long distance as early as possible. Most of the existing algorithms can detect small targets in simple backgrounds, but they would fail on the detection when the background clutters are chaotic and the signal to clutter ratio (SCR) is low. Therefore, we propose a new infrared small target detection method which called a directional-progressive search (DPS) method. Our method derives from a fact that a small target is an isotropic Gaussian distribution at a long distance, while clutters show different characteristics in different directions. Based on this difference, we decompose the original image into first-order sub-images with different directions by using a first-order directional derivative (FODD) filter. Then zero-crossing points are detected in each direction step by step to distinguish small targets and background clutters. After screening progressively, the positions where existing zero-crossing points in every sub-image can be confirmed as targets. Experimental results show that our method acquires higher detection rates and lower false alarm rates compared with other methods. At the same time, our method can still keep better performance under various complex backgrounds. The robustness of our method is strong. © 2018 IEEE.

关键词： Clutter (information theory)

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Impact of Rail Transit Systems on Transport Accessibility under Road Congestion

Impact of Rail Transit Systems on Transport Accessibility un...

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2022世界交通运输大会(WTC2022)

作者： Bo Zhan Hui Zhang Min Ouyang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Lab.for Image Processing and Intelligent Control Huazhong University of Science and TechnologyMinistry of Education

Investment inrail transit systems is widely advocated in large,high-density cities on the basis of its purported role in reducing road congestion,but the impact of rail transit systems on congestion remains a *** paper provides a method for evaluating the impact of rail transit systems on road congestion in terms of transport accessibility,and applies the method to 43 Chinese cities that have opened rail transit *** show that the rail transit systems can reduce road congestion in most cities,but their performance largely depends on the congestion speed and the rail transit network ***,the structural properties analysis of rail transit networks in two typical cities(Dalian and Changchun)indicates that rail transit lines with longer length,smaller detour factor,and larger population coverage rate tend to perform better in congestion *** findings in this paper provide guidance for city authorities regarding current and future investment in rail transit systems.

关键词： Rail transit system Congestion reduction Accessibility Rail transit network size Congestion speed Structural properties

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