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arXiv 2025年

作者： Yang, Wennuo Wu, Shiling Zhou, Yuzhi Luo, Cheng He, Xilin Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Provincial Key Laboratory of Intelligent Information Processing China HBUG Lab University of Exeter United Kingdom

Multivariate Time Series Classification (MTSC) enables the analysis if complex temporal data, and thus serves as a cornerstone in various real-world applications, ranging from healthcare to finance. Since the relationship among variables in MTS usually contain crucial cues, a large number of graph-based MTSC approaches have been proposed, as the graph topology and edges can explicitly represent relationships among variables (channels), where not only various MTS graph representation learning strategies but also different Graph Neural Networks (GNNs) have been explored. Despite such progresses, there is no comprehensive study that fairly benchmarks and investigates the performances of existing widely-used graph representation learning strategies/GNN classifiers in the application of different MTSC tasks. In this paper, we present the first benchmark which systematically investigates the effectiveness of the widely-used three node feature definition strategies, four edge feature learning strategies and five GNN architecture, resulting in 60 different variants for graph-based MTSC. These variants are developed and evaluated with a standardized data pipeline and training/validation/testing strategy on 26 widely-used suspensor MTSC datasets. Our experiments highlight that node features significantly influence MTSC performance, while the visualization of edge features illustrates why adaptive edge learning outperforms other edge feature learning methods. The code of the proposed benchmark is publicly available at https://***/CVI-yangwn/*** Codes 68T10 © 2025, CC BY.

关键词： Graph neural networks

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An Efficient Deep Learning Approach to Detect Neurodegenerative Diseases Using Retinal Images

An Efficient Deep Learning Approach to Detect Neurodegenerat...

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computer Science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Chowdhury Mohammad Irfanuddin Wasique Islam Shafin Koushik Ahmed Md. Hasib Khan Md. Ashraful Alam Rafeed Rahman Shakib Mahmud Dipto Department of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of CSE Computer Vision and Intelligent Systems (CVIS) Research Lab Brac University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh (ULAB) Dhaka Bangladesh

Neurodegenerative disorders like Multiple Sclerosis, Parkinson's disease, and Alzheimer's disease typically get diagnosed through brain MRI, CT scans, genetic testing, and various laboratory screening tests that are often tedious, time-consuming, and beyond the reach of the financial capability of many people especially, those living in the developing world. Moreover, they often pose potential hazards for patients with certain pre-existing conditions. To remedy this, we propose an efficient deep-learning approach that enables the detection of such neurodegenerative diseases through retinal images rather than brain images. Our proposed system can proactively detect such disorders simply through retinal scans, which are fast and cost-effective compared to traditional diagnostic approaches, which require expensive and sophisticated equipment. We used a dataset containing retinal cross-sectional images of 21 Multiple Sclerosis patients and 14 healthy individuals for our research, and our model achieved 98.85% accuracy while classifying healthy and diseased individuals from retinal scans.

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A Simplified Skeleton Joints Based Approach For Human Action Recognition 7

A Simplified Skeleton Joints Based Approach For Human Action...

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7th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2021

作者： Malik, Najeeb Ur Rehman Abu Bakar, Syed A.R. Sheikh, Usman Ullah Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia UTM Johor Bahru Campus Johor 81310 Malaysia

ISBN: (纸本)9781665435925

The growing technological development in the field of computer vision in general, and human action recognition (HAR), in particular, have attracted increasing number of researchers from various disciplines. Amid the variety of challenges in the field of human action recognition, one of the major issues is complex modelling which requires multiple parameters leading to troublesome training which further requires heavy configuration machines for real-time recognition. Therefore, there is a need to develop a simplified method that could result in reduced complexity, without compromising the performance accuracy. In order to address the mentioned issue, this paper proposes an approach that extracts the mean, variance and median from the skeleton joint locations and directly uses them in the classification process. The system used MCAD dataset for extracting 2D skeleton features with the help of OpenPose technique, which is suitable for the extraction of skeleton features from the 2D image instead of 3D image or using a depth sensor. Henceforth, we avoid using either the RGB images or the skeleton images in the recognition process. The method shows a promising performance with an accuracy of 73.8% when tested with MCAD dataset. © 2021 IEEE

关键词： Image processing

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Detecting Coronavirus (COVID -19) Disease Cues from Chest Radiography Images 13

Detecting Coronavirus (COVID -19) Disease Cues from Chest Ra...

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13th Indian Conference on computer vision, Graphics, and Image Processing, ICVGIP 2022

作者： Vinod, Athira Shilna Sreeni, K.G. Purushothaman, Anurenjan Jeena, R.S. Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum Kerala Trivandrum India Government Engineering College Kerala Idukki India Rajiv Gandhi Institute of Technology Kerala Kottayam India Affiliated to APJ Abdul Kalam Technological University Kerala Trivandrum India

ISBN: (纸本)9781450398237

This paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community Acquired Pneumonia (CAP) patients. The proposed neural model named, Residual C-NiN uses a modified convolutional neural network (CNN) with residual connections and a Network-in-Network (NiN) architecture for COVID-19 and CAP detection. The model is trained with the Signal Processing Grand Challenge (SPGC) 2021 COVID dataset. The proposed neural model achieves a slice-level classification accuracy of 93.54% on chest CT images and patient-level classification accuracy of 86.59% with class-wise sensitivity of 92.72%, 55.55%, and 95.83% for COVID-19, CAP, and Normal classes, respectively. Experimental results show the benefit of adding NiN and residual connections in the proposed neural architecture. Experiments conducted on the dataset show significant improvement over the existing state-of-the-art methods reported in the literature. © 2022 ACM.

关键词： COVID-19

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OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers

arXiv

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arXiv 2022年

作者： Pei, Jialun Cheng, Tianyang Fan, Deng-Ping Tang, He Chen, Chuanbo Van Gool, Luc School of Computer Science and Technology Hust China School of Software Engineering Hust China Computer Vision Lab Eth Zurich Switzerland

We present OSFormer, the first one-stage transformer framework for camouflaged instance segmentation (CIS). OSFormer is based on two key designs. First, we design a location-sensing transformer (LST) to obtain the location label and instance-aware parameters by introducing the location-guided queries and the blend-convolution feed-forward network. Second, we develop a coarse-to-fine fusion (CFF) to merge diverse context information from the LST encoder and CNN backbone. Coupling these two components enables OSFormer to efficiently blend local features and long-range context dependencies for predicting camouflaged instances. Compared with two-stage frameworks, our OSFormer reaches 41% AP and achieves good convergence efficiency without requiring enormous training data, i.e., only 3,040 samples under 60 epochs. Code link: https://***/PJLallen/OSFormer. Copyright © 2022, The Authors. All rights reserved.

关键词： Location

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Edge-aware Feature Aggregation Network for Polyp Segmentation

arXiv

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arXiv 2023年

作者： Zhou, Tao Zhang, Yizhe Chen, Geng Zhou, Yi Wu, Ye Fan, Deng-Ping PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering NPU Xi’an China School of Computer Science and Engineering Southeast University Nanjing China Computer Vision Lab ETH Zürich Zürich Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness. Our implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Maize EfficientNet Fusion: Advancing Maize Disease Detection with MF-NET

Maize EfficientNet Fusion: Advancing Maize Disease Detection...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Fatima Khalid Muhammad Hanif Qurat Ul Ain Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Topi KPK Pakistan Aerial Robotics and Vision Lab Ghulam Ishaq Khan Institute of Engineering Sciences and Engineering Topi KPK Pakistan Department of Computing Shifa Tameer-E-Millat University Islamabad Pakistan

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Maize is a vital global crop, essential for food security but highly susceptible to diseases that threaten yield and quality. Traditional methods for detecting these diseases are computationally intensive and rely on high-quality images, limiting their practical use in diverse field conditions. This research addresses these challenges by proposing MF-Net, a novel model that leverages two EfficientNet-b0 architectures with ReLU and Swish activation functions. Our approach involves partially training Model R with frozen early layers using ReLU, and fully training Model S with Swish activation for enhanced optimization and robustness. We employ model concatenation and feature fusion techniques to create a lightweight yet powerful model, complemented by additional layers for improved feature extraction and regularization. Experimental results on the Corn Disease and Severity (CD&S) dataset are compelling: MF-Net achieves a remarkable 95.7% accuracy and 96% precision in disease classification, and 88.3% accuracy and 88.46% precision in severity level detection. These results highlight the model's effectiveness under challenging conditions such as cluttered backgrounds and variable lighting, significantly reducing computation time without compromising accuracy. Hence, MF-Net presents an innovative and efficient solution for maize disease detection and severity assessment, offering significant advancements for agricultural practices and crop management.

关键词： Training Adaptation models Accuracy Computational modeling Crops Lighting computer architecture Feature extraction Robustness Diseases

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A Deep Learning Method for Floodwater Depth Prediction on Roadways from Side-View Real and Synthetic Images of Vehicles

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Journal of Big Data Analytics in Transportation 2022年第1期4卷 85-101页

作者： Sazara, Cem Salahshour, Behrouz Cetin, Mecit Iftekharuddin, Khan Transportation Research Institute Old Dominion University Norfolk USA Vision Lab Electrical and Computer Engineering Department Old Dominion University Norfolk USA

This work proposes a novel deep learning method to predict depth of floodwater on streets based on side-view images of vehicles. Finding flood levels on roadways is useful for various applications including routing of emergency vehicles and safe traffic management and operations. The proposed approach consists of four main steps: semantic segmentation, object detection, segmentation refinement, and water depth calculation. The semantic segmentation step segments the flood scene to floodwater, wheels, and background classes; object detection step detects tires of the vehicles; and segmentation refinement step improves the initial segmentation result. Water depth is then calculated by fitting a line to the water edges on each tire and fitting an ellipse to each wheel on the vehicle. Finally, the water depth is estimated relative to the wheel size without knowing the actual wheel and tire dimensions which may vary from vehicle to vehicle. If the actual vehicle wheel dimensions are known, which can be inferred from the vehicle make/model, the proposed method is expected to offer more accurate water depth estimation. This method is trained using both real-world flooding images curated from web search and synthetic images from flooded city streets generated using the Unity software. We use only the real-world images in validation and test processes to tune the models and measure their performance with real-world examples. Overall, the proposed method achieves 0.038 mean absolute error (MAE) on the test set with 45 real-world images.

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FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments

arXiv

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arXiv 2024年

作者： Wei, Hexiang Jiao, Jianhao Hu, Xiangcheng Yu, Jingwen Xie, Xupeng Wu, Jin Zhu, Yilong Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Robot Perception and Learning Lab Department of Computer Science University College London United Kingdom Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China Guangzhou China

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises 27 sequences, spanning over 2.5 hours and collected from four distinct platforms: a handheld suite, a legged robot, a unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of 38.7km. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately 0.3km2. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The completae dataset, including sensor data, GT, and calibration details, is accessible at https://***/dataset/fusionportable v2. Copyright © 2024, The Authors. All rights reserved.

关键词： SLAM robotics

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vision Transformer Based Automated Model for Enhancing Lung Cancer Classification

Vision Transformer Based Automated Model for Enhancing Lung ...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Akbar Sheikh Akbari Arvind Kumar B Ramachandra Reddy Koushlendra Kumar Singh Masahiro Takei Leeds Beckett University UK Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur Jharkhand India Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Department of Mechanical Engineering Chiba University Chiba Japan

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

Lung cancer is one of the leading causes of cancer related mortality. The early detection and classification of the cancers tissues will reduce the mortalities rate. The present research focus on the development of automated classification model for lung and colon cancers tissues based on the histopathology images. The present work encompasses a vision transformer (ViT) based model to enhance diagnostic accuracy of lung cancers tissues. The proposed model utilizes the self-attention mechanism of ViT to focus on essential features present in histopathologicals images. The proposed model has been validated using two different dataset namely LC25000 & IQ-OTH/NCCD with 25000 & 1096 images respectively. The performance of proposed model is compared with traditional convolutional neural network (CNN) model and it has been observed the based model outforms better in terms of accuracy which - 98.80% & 99.09% respectively for datasets.

关键词： computer vision Analytical models Accuracy Lungs Lung cancer Imaging Transformers Convolutional neural networks Reliability Testing

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