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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是111-120 订阅

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RBINet: A Convolutional Neural Network for Automatic Building Identification 26

RBINet: A Convolutional Neural Network for Automatic Buildin...

引用

26th International conference on computer and Information Technology, ICCIT 2023

作者： Diba, Nishat Tasnime Chowdhury, S.A.H. Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Convolutional neural networks (CNNs) have been used to analyze visual data in several applications related to computer vision, including image classification and segmentation. the aim of this paper is to use a trained CNN model to accurately recognize various building from images. However, the exploration of this task has been hampered by the lack of well-organized, sizable architectural picture datasets with a particular focus on building identification. To address this, we created a dataset of building pictures with related labels to train and test our CNN model for building identification. the name of the dataset is the RUET-building image (RBI) dataset as the photographed buildings are located at the RUET (a prestigious engineering University) campus. there are 7075 photos in this collection, which were gathered from 15 distinct buildings. In order to find the ideal model for the RBI dataset, this work also contains an ablation analysis of our proposed CNN named RBINet (RUET-building identification network). In terms of building recognition accuracy, RBINet (with four convolutional layers and three hidden layers) outperforms leading models and achieves 98.88% accuracy. the authors also report that the EfficientNetb0 and DenseNet121 models give the second (98.44%) and third (98.22%) highest classification accuracy, respectively, for the RBI dataset. © 2023 ieee.

关键词： Building identification building structure recognition (BSR) convolutional neural networks (CNNs) RUET-building identification network (RBINet)

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DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points 26

DenseGAP: Graph-Structured Dense Correspondence Learning wit...

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Kuang, Zhengfei Li, Jiaman He, Mingming Wang, Tong Zhao, Yajie Univ Southern Calif Los Angeles CA USA USC Inst Creat Technol Los Angeles CA USA Stanford Univ Stanford CA USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Establishing dense correspondence between two images is a fundamental computer vision problem, which is typically tackled by matching local feature descriptors. However, without global awareness, such local features are often insufficient for disambiguating similar regions. And computing the pairwise feature correlation across images is both computation-expensive and memory-intensive. To make the local features aware of the global context and improve their matching accuracy, we introduce DenseGAP, a new solution for efficient Dense correspondence learning with a Graph-structured neural network conditioned on Anchor Points. Specifically, we first propose a graph structure that utilizes anchor points to provide sparse but reliable prior on inter- and intra-image context and propagates them to all image points via directed edges. We also design a graph-structured network to broadcast multi-level contexts via light-weighted message-passing layers and generate high-resolution feature maps at low memory cost. Finally, based on the predicted feature maps, we introduce a coarse-to-fine framework for accurate correspondence prediction using cycle consistency. Our feature descriptors capture both local and global information, thus enabling a continuous feature field for querying arbitrary points at high resolution. through comprehensive ablative experiments and evaluations on large-scale indoor and outdoor datasets, we demonstrate that our method advances the state-of-the-art of correspondence learning on most benchmarks.

关键词： computer vision Costs Correlation Image edge detection Neural networks Estimation pattern recognition

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Graph Data Augmentation for Node Classification 26

Graph Data Augmentation for Node Classification

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Wei, Ziyu Xiao, Xi Zhang, Bin Hu, Guangwu Li, Qing Xia, Shutao Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Chinese Acad Sci Inst Informat Engn Key Lab Network Assessment Technol Beijing Peoples R China Peng Cheng Lab Dept New Networks Shenzhen Peoples R China Shenzhen Inst Informat Technol Shenzhen Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In recent years, Graph Neural Networks (GNNs) have emerged as powerful techniques for graph-structure data, which are essential for a wide range of graph-based tasks like link prediction and node classification. However, over-smoothing and over-fitting are two main challenges that impact negatively on model performance. Data augmentation is a good solution to these two problems, and it is also proven very effective in computer vision and nature language processing. But there is a relatively small body of literature when it comes to graph data augmentation. In this paper, we propose a Graph Data Augmentation (GDA) strategy to optimize the graph topology for node classification tasks. Our GDA approach consists of two operations: edge manipulation based on similarities of node pairs (GDA-E) and new nodes addition to under-informed old nodes (GDA-N). GDA-E is designed to add missing edges and remove noisy edges, while GDA-N is established to help nodes with low degree. Both operations can improve the information-to-noise ratio of the whole graph and lead to better performance of GNNs. the comparative results of experiments on three different datasets show that our GDA approach achieves considerable improvement (11.0% average) over origin graphs, and the ablation study verifies the effectiveness of both GDA-E and GDA-N.

关键词： computer vision Computational modeling computer architecture Benchmark testing Graph neural networks Topology pattern recognition

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MSFFAR-Net: A Multi-Scale Feature Fusion and Attention-Guided Residual Network for Bangladeshi Medicinal Plants Classification 26

MSFFAR-Net: A Multi-Scale Feature Fusion and Attention-Guide...

引用

26th International conference on computer and Information Technology, ICCIT 2023

作者： Islam, Al Mohimanul Masud, Fatiha Binta Ahmed, Md. Rayhan Islam, Salekul United International University Dept. of Computer Science and Engineering Dhaka Bangladesh University of British Columbia Dept. of Computer Science Kelowna Canada

ISBN: (纸本)9798350359015

the classification of plant species, particularly medicinal ones, has gained much research attention due to challenges in manual identification and the urgency to protect endangered species. the growing need for automated identification of medicinal plants has led to a focus on using leaf images, which provide abundant information and are easily accessible. the importance of medicinal plants in healthcare underscores the need for efficient identification methods, given the time-consuming reliance on experts. vision-based systems offer fast and accurate recognition, benefiting researchers and the public in identifying these essential plant species. this work proposed a Multi-Scale Feature Fusion and Attention-Guided Residual Network (MSFFAR-Net) that incorporates pre-trained MobileNetv2 as feature extractor and a series of newly introduced Attention Residual Convolution Module to classify Bangladeshi Medicinal plants. Our model, validated through a 4-fold cross-validation, achieved high classification mean accuracy of 98.12% for BDMediLeaves and 96.18% for the Bangladeshi Medicinal Plant Dataset. © 2023 ieee.

关键词： Bangladeshi medicinal plant Classification Convolutional Neural Network Deep learning Transfer learning

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GaussianMask: Uncertainty-aware Instance Segmentation based on Gaussian Modeling 26

GaussianMask: Uncertainty-aware Instance Segmentation based ...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Lee, Seung Il Kim, Hyun Seoul Natl Univ Sci & Technol Seoul Res Ctr Elect & Informat Technol Dept Elect & Informat Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Instance segmentation, which has been required in various applications in recent years, is aimed at reliable bounding box (bbox) detection (i.e., localization) and stable mask prediction (i.e., segmentation). However, the mask uncertainty problem is still unresolved, which hinders the ability to achieve an accurate instance segmentation. In this paper, we propose GaussianMask, an uncertainty-aware instance segmentation technique based on Gaussian modeling. We can determine the uncertainty of the network through the variance of masks extracted by redesigning the loss function based on Gaussian modeling, and the mask accuracy can be improved by constructing a robust model that adaptively applies this uncertainty to the network. In particular, the additional computations caused during this process are insignificant, and thus negligible for the processing speed of the network. Moreover, GaussianMask has the advantage of being applicable to any network due to its high compatibility. Experimental results show that the mask average precision (AP) of the representative instance segmentation models, YOLACT and Mask R-CNN, increases from 29.7% to 31.8% and from 35.7% to 37.5%, respectively, on the MS-COCO dataset.

关键词： Location awareness Adaptation models computer vision Uncertainty Computational modeling computer network reliability Predictive models

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Automatic Detection and Fitting of Ellipse Markers Using EllipseNet 26

Automatic Detection and Fitting of Ellipse Markers Using Ell...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Qian, Zhengda Tang, Fulin Liu, Bingxi Fu, Yujie Wu, Shaohuan Jia, Xiaohong Wu, Yihong Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Shandong Univ Sch Math & Stat Jinan Shandong Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Ellipses are important elements in projective geometry. Accurate extraction of ellipse information is the first step in many computer vision applications, such as ellipse-based camera calibration and camera pose estimation. At present, most ellipse detection algorithms rely on the edge features extracted by Canny, which leads to a number of wrong detection results since non-ellipse edges features are also involved. To address this problem, this paper proposes a novel ellipse marker detection neural network, called EllipseNet. Notably, we propose a new loss function to enhance the rotation perception ability of EllipseNet. Furthermore, a novel ellipse marker data enhancement method is proposed for saving the time cost of labelling ellipse parameters. Experiments show that EllipseNet can improve the detection precision of ellipse regions by more than 3% improvement compared with other SOTA general object detectors.

关键词： Training Costs Image edge detection Pose estimation Neural networks Feature extraction Cameras

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HSFV-based Action recognition Using Recurrent Neural Networks 26

HSFV-based Action Recognition Using Recurrent Neural Network...

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26th International conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Haozhe Dai, Hua Yang, Weihong Zheng, Guineng Lu, Meng Ji, Xiaofei Nanjing University of Posts and Telecommunications Nanjing210023 China University of Utah Salt Lake City84112 United States Co Ltd Qingdao266520 China Qingdao University Qingdao266071 China

ISBN: (纸本)9798350331684

Human action recognition is one of the basic problems in the field of computer vision, which has a wide range of applications in video surveillance, sports analysis, medical care. Since the skeleton data can not be easily affected by background and views and has small computational cost, skeleton-based action recognition has attracted a lot of researchers' interest. In recent years, some researchers have proposed to use the joints connection methods to mine the intrinsic correlation between skeleton joints as the feature representation of motion skeleton, and good results are achieved. However, there is still the problem of confusion when distinguishing actions with similar motion segments. To solve this problem, a human skeleton feature vector model (HSFV) is proposed in this paper. By constructing the feature extraction reference frame, the model uses OKS indicator to calculate and generate the feature vector describing the human posture. the feature vectors are input into the recurrent neural network, the experimental results on public and self-built datasets show that the human skeleton feature vector model based action recognition method proposed in this paper can distinguish actions with similar motion segments, and has the advantages of simple training and small calculation cost. It has broad application prospects in the fields of process detection, motion evaluation and so on. © 2023 ieee.

关键词： Recurrent neural networks

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Emotion recognition through computer vision: Challenges, Techniques, and Applications 26

Emotion Recognition Through Computer Vision: Challenges, Tec...

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26th International conference on Industrial Technology, ICIT 2025

作者： Oliveira, Argemiro Pellenz, Marcelo Teixeira, Marco PPGia-PUCPR Parana Curitiba Brazil

ISBN: (纸本)9798331521950

this study presents a comprehensive analysis of methods, techniques, and technologies employed in facial emotion recognition in human-robot interaction (HRI). A total of 124 articles were reviewed, selected based on specific criteria, resulting in 80 relevant studies for analysis. Techniques such as CNNs, Transfer Learning, and ViT stand out for their effectiveness, while approaches like HOG and LBP remain relevant in lowcost scenarios. Widely used datasets, such as FER-2013 and CK+, demonstrate robustness, and hardware solutions range from highperformance setups to accessible options like Google Colab. the study concludes by highlighting the importance of multimodal techniques and the potential of approaches such as ViT and MTL, proposing strategies to overcome variability challenges and adapt to real classroom conditions. © 2025 ieee.

关键词： Educational robots

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Attention Trinity Net and DenseNet Fusion: Revolutionizing American Sign Language recognition for Inclusive Communication 26

Attention Trinity Net and DenseNet Fusion: Revolutionizing A...

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26th International conference on computer and Information Technology, ICCIT 2023

作者： Joy, Tanvir Shakil Hossain Efat, Anwar Mahedy Hasan, S.M. Jannat, Nahrin Oishe, Mahjabin Mitu, Mostarina Montashir Fahim, A.S.M. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

American Sign Language (ASL) is a unique and essential language for the North American deaf and hard-of-hearing community, with distinct grammar and expressive hand and facial movements. In this study, we embarked on a unique journey to enhance ASL recognition, recognizing the significance of this language as a means of communication. Our approach began by consolidating two distinct datasets, amalgamating their valuable images to create a comprehensive corpus for classification. this fusion of resources provided a robust foundation for our research. A hallmark of our methodology is the incorporation of three sophisticated attention modules: channel attention, soft attention, and squeeze and excitation attention. these modules collectively form the framework known as Attention Trinity Net (ATN), a testament to our commitment to precision and performance. Furthermore, we introduced the concept of Uni Attention Network (UAN), an innovative approach that leverages DenseNet121, a powerful neural network architecture. Each attention module, channel attention, soft attention, and squeeze and excitation attention, is thoughtfully integrated into the UAN framework, enriching its ability to discern intricate ASL gestures. In a climactic juncture of our study, we seamlessly combined the outputs of the three UANs based on the ATN to form a cohesive and comprehensive prediction model. the synergy achieved through this integration resulted in an astounding accuracy rate of 99.98%, surpassing the performance of any existing ASL recognition models. Our study is a significant milestone in ASL recognition, highlighting the potential of advanced attention mechanisms and unified network structures in deep learning and computer vision. this achievement benefits the deaf and hard-of-hearing community and advances artificial intelligence and human-computer interaction. © 2023 ieee.

关键词： American Sign Language (ASL) recognition Attention Trinity Net (ATN) DenseNet121 Uni Attention Network (UAN)

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A New Video Dataset for Recognizing Intake Gestures in a Cafeteria Setting 26

A New Video Dataset for Recognizing Intake Gestures in a Caf...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Tang, Zeyu Hoover, Adam Clemson Univ Dept Elect & Comp Engn Clemson SC 29634 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Intake gesture detection plays an important role in dietary monitoring systems to help individuals objectively quantify their eating behavior. Intake gestures include motions related to consumption, such as ingestion of a bite (food) and drink (liquid). Video provides an automatic and non-contact way to detect gestures. Previous studies have evaluated video-based detectors in laboratories where participants were invited to consume meals containing a limited number of food and beverage choices. Because of this, previous datasets have limited variability in food and beverage appearances, and types of intake tools and gestures. Cameras were positioned conspicuously close to participants. To address these limitations, we describe a new video dataset that was collected by recording the meals of 264 participants in a real restaurant. Participants consumed 374 different types of foods and beverages. the camera was installed on the ceiling of the restaurant to minimize the effect of an experimental observer. In this study, we present the details of collecting and labeling the data for video-based research. To explore the feasibility of detecting intake gestures using our dataset and establish baseline performance, we implemented three state-of-art models, namely CNN-LSTM, SlowFast and X3D-L. Results indicate that the best F1 scores were 0.899 and 0.778 on bite and drink gesture detection respectively.

关键词： Intake gesture detection computer vision video processing neural network deep learning

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