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2023 International Conference on Digital image Computing: Techniques and Applications, DICTA 2023

作者： Zhou, Jieming Zhang, Tong Fang, Pengfei Petersson, Lars Harandi, Mehrtash Australian National University College of Engineering and Computer Science Canberra Australia Image and Visual Representation Laboratory Epfl Lausanne Switzerland Imaging and Computer Vision Group Csiro Canberra Australia Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350382204

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs' designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model's performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks. © 2023 IEEE.

关键词： Graph neural networks

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Spatio-temporal Attention Graph Convolutions for Skeleton-based Action Recognition 23rd

Spatio-temporal Attention Graph Convolutions for Skeleton-b...

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22nd Scandinavian Conference on image analysis, SCIA 2023

作者： Le, Cuong Liu, Xin Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

ISBN: (纸本)9783031314346

In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the spatial correlations between human joints and often overlook the temporal relationships. In an action sequence, the consecutive frames in a neighborhood contain similar poses and using only temporal convolutions for extracting local features limits the flow of useful information into the calculations. In many cases, the discriminative features can present in long-range time steps and it is important to also consider them in the calculations to create stronger representations. We propose an attentional graph convolutional network, which adapts self-attention mechanisms to respectively model the correlations between human joints and between every time steps for skeleton-based action recognition. On two common datasets, the NTU-RGB+D60 and the NTU-RGB+D120, the proposed method achieved competitive classification results compared to state-of-the-art methods. The project’s GitHub page: STA-GCN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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UformPose: A U-Shaped Hierarchical Multi-Scale Keypoint-Aware Framework for Human Pose Estimation

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IEEE Transactions on Circuits and Systems for Video Technology 2023年第4期33卷 1697-1709页

作者： Wang, You-Jie Luo, Yan-Min Bai, Gui-Hu Guo, Jing-Ming Huaqiao University Key Laboratory for Computer Vision and Pattern Recognition The College of Computer Science and Technology Xiamen361021 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan

Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we present a novel pose estimation framework named UformPose which targets to relieve these issues. UformPose has two core designs: Shared Feature Pyramid Stem (SFPS) and U-shaped hierarchical Multi-scale Keypoint-aware Attention Module (U-MKAM). SFPS is a feature pyramid stem with shared mechanism to learn stronger low-level features at the initial stage, and the shared mechanism can facilitate cross-resolution commonality learning. Our U-MKAM attempts to generate high-quality high-resolution representations by integrating all levels of feature representation of the backbone layer by layer. More importantly, we utilize the flexibility of attention operations for keypoint-aware modeling, which explicitly captures and trades-offs the dependencies between keypoints. We empirically demonstrate the effectiveness of our framework through the competitive pose estimation results on the COCO dataset. Extensive experiments and visual analysis on CrowdPose demonstrate the robustness of our model in crowd scenes. © 1991-2012 IEEE.

关键词： computer vision

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Spatial Quality Assessment of Pansharpened images Based on Gray Level Co-Occurrence Matrix 12

Spatial Quality Assessment of Pansharpened Images Based on G...

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12th Iranian/2nd International Conference on Machine vision and image Processing, MVIP 2022

作者： Aghapour Maleki, Shiva Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Assessing the quality of pansharpened images is a critical issue in order to obtain a quantitative score to represent the quality and compare the performance of different fusion methods. Most of the introduced metrics for pansharpened image quality assessment, evaluate the spectral content of the image, while in different applications of remote sensing like detection and identification of image objects, spatial quality has an important role. In the current study, a new index for spatial quality assessment is introduced that extracts gray level co-occurrence matrix (GLCM) from distorted and reference images and compares the similarities of these features. The tempere image database 2013 (TID2013) that provides reference and different types of distorted images with subjective scores of each image is used as the desired database. To solve the high computational complexity of obtaining GLCM features, the fast GLCM method is employed. In this way, 16 different features are extracted. To select the features that have the most consistency with the human visual system (HVS), the forward floating search method is used as a feature selection method and five features are obtained as the final features to form the desired index. Experimental results show the efficiency of the proposed method in determining the spatial quality of fused images compared with that of the available quality assessment metrics. © 2022 IEEE.

关键词： Remote sensing

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Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

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Building Simulation 2023年第7期16卷 1219-1238页

作者： Cary A.Faulkner Dominik S.Jankowski John E.Castellini Jr Wangda Zuo Philipp Epple Michael D.Sohn Ali Taleb Zadeh Kasgari Walid Saad Department of Mechanical Engineering University of Colorado BoulderBoulderCOUSA HySON Institut SonnebergGermany Department of Architectural Engineering Pennsylvania State UniversityUniversity ParkPAUSA Department of Mechanical Engineering Coburg University of Applied SciencesCoburgGermany Energy Analysis and Environmental Impacts Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Electrical and Computer Engineering Virginia TechArlingtonVAUSA

Prediction of indoor airflow distribution often relies on high-fidelity,computationally intensive computational fluid dynamics(CFD)*** intelligence(Al)models trained by CFD data can be used for fast and accurate prediction of indoor airflow,but current methods have limitations,such as only predicting limited outputs rather than the entire flow ***,conventional Al models are not always designed to predict different outputs based on a continuous input range,and instead make predictions for one or a few discrete *** work addresses these gaps using a conditional generative adversarial network(CGAN)model approach,which is inspired by current state-of-the-art Al for synthetic image *** create a new Boundary Condition CGAN(BC-CGAN)model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter,such as a boundary ***,we design a novel feature-driven algorithm to strategically generate training data,with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the Al *** BC-CGAN model is evaluated for two benchmark airflow cases:an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated *** also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error *** results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5%relative error and up to about 75,ooo times faster when compared to reference CFD *** proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the Al models while maintaining prediction accuracy,particularly when the flow changes non-linearlywith respectto an input.

关键词： artificial intelligence indoor airflow conditional generative adversarial network computational fluid dynamics

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TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud analysis

TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cl...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Pavlo Melnyk Andreas Robinson Michael Felsberg Mårten Wadenbäck Department of Electrical Engineering Computer Vision Laboratory Linköping University Sweden

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In many practical applications, 3D point cloud analy-sis requires rotation invariance. In this paper, we present a learnable descriptor invariant under 3D rotations and reflections, i.e., the O(3) actions, utilizing the recently intro-duced steerable 3D spherical neurons and vector neurons. Specifically, we propose an embedding of the 3D spherical neurons into 4D vector neurons, which leverages end-to-end training of the model. In our approach, we perform TetraTransform-an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons-and ex-tract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework, termed TetraSphere, negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally, TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus, our results reveal the prac-tical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https: //***/pavlo-melnyk/tetrasphere.

关键词： Point cloud compression Training Solid modeling Three-dimensional displays Shape Neurons Vectors

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Quantitative comparison between reflection and transmission modes for the inspection of composite materials using pulsed thermography 45

Quantitative comparison between reflection and transmission ...

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Thermosense: Thermal Infrared Applications XLV 2023

作者： Alhammad, M. Avdelidis, N.P. Ibarra-Castanedo, C. Torbali, M.E. Zolotas, A. Maldague, X.P.V. School of Aerospace Transport and Manufacturing Cranfield University United Kingdom Universite Laval Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Quebec CityG1V 0A6 Canada

ISBN: (纸本)9781510661868

Composite materials have extensively been used in the aerospace industry. There are several inspection methods to ensure the safety of these composites;pulse thermography (PT) is one of the most promising ones. Both reflection and transmission modes of PT could be applied. However, few studies reveal the advantages and disadvantages of these two modes. This paper presents a quantitative comparison of pulsed thermal imaging in two modes of reflection and gradual heat transfer in carbon fibres. Experimental work was conducted on carbon fibre-reinforced plastic (CFRP) samples with different thicknesses and thermal images were recorded in both modes. Thermal images were further processed using statistical analysis and machine learning algorithms. Comparing the results from both modes, there is a marked improvement in the accuracy when the reflection mode is employed. © 2023 SPIE.

关键词： Nondestructive examination

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern analysis and Machine Intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： image registration

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting

arXiv

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arXiv 2024年

作者： Zhang, Jinwei Zuo, Lianrui Liu, Yihao Remedios, Samuel Landman, Bennett A. Prince, Jerry L. Carass, Aaron Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States

Automatic magnetic resonance (MR) image processing pipelines are widely used to study people with multiple sclerosis (PwMS), encompassing tasks such as lesion segmentation and brain parcellation. However, the presence of lesion often complicates these analysis, particularly in brain parcellation. Lesion filling is commonly used to mitigate this issue, but existing lesion filling algorithms often fall short in accurately reconstructing realistic lesion-free images, which are vital for consistent downstream analysis. Additionally, the performance of lesion segmentation algorithms is often limited by insufficient data with lesion delineation as training labels. In this paper, we propose a novel approach leveraging Denoising Diffusion Implicit Models (DDIMs) for both MS lesion filling and synthesis based on image inpainting. Our modified DDIM architecture, once trained, enables both MS lesion filing and synthesis. Specifically, it can generate lesion-free T1-weighted or FLAIR images from those containing lesions;Or it can add lesions to T1-weighted or FLAIR images of healthy subjects. The former is essential for downstream analyses that require lesion-free images, while the latter is valuable for augmenting training datasets for lesion segmentation tasks. We validate our approach through initial experiments in this paper and demonstrate promising results in both lesion filling and synthesis, paving the way for future work. Our code is available at https://***/IACL/UponAcceptance. © 2024, CC BY.

关键词： image segmentation

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