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共 1490 条记录，以下是91-100 订阅

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Golf Swing Sequencing Using Computer Vision 10th

Golf Swing Sequencing Using Computer Vision

引用

10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Marais, Marc Brown, Dane Rhodes Univ Dept Comp Sci ZA-6140 Grahamstown South Africa

ISBN: (纸本)9783031048814;9783031048807

analysis of golf swing events is a valuable tool to aid all golfers in improving their swing. image processing and machine learning enable an automated system to perform golf swing sequencing using images. The majority of swing sequencing systems implemented involve using expensive camera equipment or a motion capture suit. An image-based swing classification system is proposed and evaluated on the GolfDB dataset. The system implements an automated golfer detector combined with traditional machine learning algorithms and a CNN to classify swing events. The best performing classifier, the LinearSVM, achieved a recall score of 88.3% on the entire GolfDB dataset when combined with the golfer detector. However, without golfer detection, the pruned VGGNet achieved a recall score of 87.9%, significantly better (>10.7%) than the traditional machine learning models. The results are promising as the proposed system outperformed a Bi-LSTM deep learning approach to achieve swing sequencing, which achieved a recall score of 76.1% on the same GolfDB dataset. Overall, the results were promising and worked towards a system that can assist all golfers in swing sequencing without expensive equipment.

关键词： Golf swing sequencing Histogram of oriented gradients Linear Discriminate analysis Support Vector Machines Gradient Boosting Machines Deep learning

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External attention augmented Swin-UNet network for semi-supervised cardiac image segmentation 3

External attention augmented Swin-UNet network for semi-supe...

引用

3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Ding, Hong Wang, Shaoyu Huang, Zhexin Chen, Nuo Qin, Zhidong Shi, Xiujin School of Computer Science and Technology Donghua University Shanghai China

ISBN: (纸本)9781510667563

Cardiac image segmentation is an important step in clinical diagnosis of heart diseases. To this day, CNNs based UNet-like networks have been applied successfully in cardiac image segmentation, yet they still fail in taking full advantage of global semantic information due to the limits of the convolution operation. On the other hand, annotation of cardiac images is a time-consuming and laborious task and requires the participation of experts. In this paper, we proposed an External Attention Augmented SwinUNet (EA-SwinUNet) network for semi-supervised cardiac image segmentation. First, we proposed a novel mean teacher semi-supervised architecture to cardiac segmentation network. Second, to overcome the limits of CNNs, we designed an improved SwinUNet segmentation network to leverage the global semantic features. By introducing external attention mechanism in skip connections, it can help network overcome the accuracy impact of small datasets and enhance the feature association between global samples. To validate our EA-SwinUNet, we conducted several experiments on the ACDC dataset and our network achieves a Dice score of 77.31% and a 95%HD of 7.03. © 2023 SPIE.

关键词： Semantics

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image Manipulation Detection and Classification Using SIFT and SURF with Support Vector Machine Classifier 3

Image Manipulation Detection and Classification Using SIFT a...

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3rd IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Ramadan, Ghazi Mohamad Naser, Zamen Latef Jagadish, Sripelli Phadke, Rekha Almusawi, Muntather College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science & Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9798350317022

Enhance digital trust and integrity by mastering image forgery detection in machine learning. Uncover the hidden truth behind visuals, ensuring authenticity in a world inundated with digital content. Various image manipulation detection machines have been innovated in the past, but these models face limitations like demanding domain expertise, hyperparameter tuning, time-consuming processes, and overlapping issues. Initially employ the histogram equalization to enhance dataset images, reducing unwanted noise and generating synthetic data for class balance. The pre-processed images are then fed into the model for ongoing analysis. In this paper, propose a Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF)-based SVM for image manipulation detection and classification, achieving outstanding performance metrics on the CASIA 1.0 dataset: 0.96 accuracy, 0.8120 IOU score, 0.7710 MCC, 0.8850 F1 score, 0.98 precision, and 0.92 sensitivity. This model surpasses the capabilities of existing models, including the Two-Stream Boundary-aware Network, Discrete Cosine Transformation (DCT)-based multitask learning network, and Multilevel Local Binary pattern Texture Descriptor. © 2023 IEEE.

关键词： & Splicing Copy-move image Manipulation Scale-Invariant Feature Transform Support Vector Machine

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Research on Electricity Consumption Characteristic analysis and Load Identification Method Based on Symmetrized Dot pattern Information Fusion

Research on Electricity Consumption Characteristic Analysis ...

引用

Energy Engineering and Power Systems (EEPS), International conference on

作者： Le Li Zhiyuan Liu Hao Han Chaoran Li Nan Liu Xiaoxun Zhu Sales Department State Grid Beijing Daxing Electric Power Supply Company Beijing China Department of Power Engineering North China Electric Power University Baoding China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

Based on multi-label load identification, power load feature analysis and intelligent identification method based on Symmetrized Dot pattern (SDP) information fusion. This method improves the decomposition efficiency and solves the problem of large reconstruction error; proposes the method of SDP fusion feature analysis, extracts the SDP image feature of each modal information of the load, improves the completeness of the information; proposes the SDP image recognition method based on YOLOv5 and builds on load intelligent recognition model. Through experimental research, the load identification accuracy of this method reached 98%, which ensured the level of non-invasive load monitoring.

关键词： YOLO Visualization Load monitoring Technological innovation image recognition Accuracy Electricity Optimal scheduling Feature extraction Power grids

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From Captions to Explanations: A Multimodal Transformer-based Architecture for Natural Language Explanation Generation 10th

From Captions to Explanations: A Multimodal Transformer-base...

引用

10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Rio-Torto, Isabel Cardoso, Jaime S. Teixeira, Luis F. INESC TEC Porto Portugal Univ Porto Fac Ciencias Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783031048814;9783031048807

The growing importance of the Explainable Artificial Intelligence (XAI) field has led to the proposal of several methods for producing visual heatmaps of the classification decisions of deep learning models. However, visual explanations are not sufficient because different end-users have different backgrounds and preferences. Natural language explanations (NLEs) are inherently understandable by humans and, thus, can complement visual explanations. Therefore, we introduce a novel architecture based on multimodal Transformers to enable the generation of NLEs for image classification tasks. Contrary to the current literature, which models NLE generation as a supervised image captioning problem, we propose to learn to generate these textual explanations without their direct supervision, by starting from image captions and evolving to classification-relevant text. Preliminary experiments on a novel dataset where there is a clear demarcation between captions and NLEs show the potential of the approach and shed light on how it can be improved.

关键词： Natural language explanations Explainable AI Transformers Natural language generation image captioning

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Weighted Features Based Classification of Polarimetric SAR images

Weighted Features Based Classification of Polarimetric SAR I...

引用

Iranian Machine Vision and image Processing (MVIP)

作者： Fatemeh Saneipour Maryam Imani Hassan Ghassemian Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Today, classification of polarimetric images is an important topic where various statistical pattern recognition methods have been used to achieve the high accurate classification maps. In this work, weighting the polarimetric features according to their statistical behavior (the mean vector and variance values as the first and second statistics) is suggested to improve the PolSAR image classification. A weighted feature matrix is composed and applied to the popular classifiers such as maximum likelihood, K-nearest neighbor and support vector machine. The weighted feature matrix can be also implemented on other arbitrary classifiers to improve their discrimination ability. The experiments on the L-band AIRSAR dataset show appropriate classification results.

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Research on image stitching algorithm based on improved SURF features 3

Research on image stitching algorithm based on improved SURF...

引用

3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Chen, Weiquan Chen, Huangfei Chen, Jisong Yang, Zhiwei Guangzhou Huali College Guangzhou China

ISBN: (纸本)9781510667563

To address the problems of uneven feature points extracted by traditional SURF algorithm in image stitching, low correct matching rate and high time complexity, this paper proposes a fast image stitching algorithm based on improved SURF features. Firstly, we use the advantages of robustness and low complexity of SURF algorithm to detect feature points;secondly, we use the advantages of FLANN algorithm to adjust parameters for accuracy improvement to match feature points;thirdly, we use RANSAC algorithm to remove feature points with incorrect matching and perform the calculation of single response matrix to find the best model matching pair and reduce the error;finally, we use OpenCV Stitcher is used for image stitching and fusion. The experiments show that the algorithm has better robustness than the traditional SURF algorithm under various complex situations such as image angle rotation, brightness change, scale scaling, etc., and can better improve improve the matching accuracy and matching efficiency. © 2023 SPIE.

关键词： image enhancement

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Proposal of a Comparative Framework for Face Super-Resolution Algorithms in Forensics 10th

Proposal of a Comparative Framework for Face Super-Resolutio...

引用

10th iberian conference on pattern recognition and image analysis (IbPRIA)

作者： Salguero-Cruz, Antonio Latorre-Carmona, Pedro Ignacio Garcia-Osorio, Cesar Univ Burgos Dept Ingn Informat Avda Cantabria S-N Burgos 09006 Spain

ISBN: (纸本)9783031048814;9783031048807

The number of images acquired by electronic devices is growing exponentially. This is partly due to an easier and more extended access to high-tech portable acquisition devices. These devices could record people of interest, either because they were involved in some type of criminal act, or they could be missing people, so their identification (and specifically the identification of their faces) is very relevant. On the other hand, image quality sometimes does not allow a positive identification of these individuals. This paper presents a framework for the comparative analysis of face super-resolution (hallucination) algorithms in forensics. The super-resolved images could be used to help identify a person of interest, and subsequently be admitted by the competent judicial authority in a criminal process, maintaining, like the rest of evidence, the so-called chain of custody, as required by the Spanish law and other similar legal systems.

关键词： Face hallucination Perceptual image quality Identification Forensics

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Advancing Brain Tumor Diagnosis: A Hybrid Approach Using Edge Detection and Deep Learning 27th

Advancing Brain Tumor Diagnosis: A Hybrid Approach Using Edg...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Vu, Ha Anh Vajda, Szilárd Department of Computer Science Central Washington University EllensburgWA98926 United States

ISBN: (纸本)9783031782008

Brain tumor classification from MRI scans demands precise image analysis, a challenge compounded by the variable morphology and location of tumors. Addressing this, our study presents an innovative approach that combines edge detection with a hierarchical deep learning framework to classify brain tumors accurately. This method enhances edge clarity, facilitating the deep learning model’s ability to distinguish between meningioma, glioma, and pituitary tumors. By deploying a two-stage model, initially segregating a meta-tumor class and pituitary and subsequently refining the meta-tumor class into glioma and meningioma with a binary classifier, we capitalize on the strengths of both traditional image processing and advanced neural networks. The already proven ResNet50 architecture, our model’s backbone, benefits from transfer learning, enabling efficient feature extraction from the edge image tailored to brain tumor recognition. Our results, evidenced by an over 96% overall accuracy rate obtained on a large benchmark brain tumor dataset, underscore the potential of integrating edge detection processing with deep learning. This integrative multi-level strategy promises to streamline the diagnostic process, offering a reliable, fast, and cost-effective solution that could reduce the need for expensive human specialist intervention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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image Processing of the Special Sensor Microwave/imager Based on Passive Microwave Remote Sensing 3

Image Processing of the Special Sensor Microwave/Imager Base...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Wang, Peng Jiang, Qigang Liu, Bin College of Geoexploration Science and Technology Jilin University China

ISBN: (纸本)9781510667563

Most of the land surface water in the plateau region is frozen. Due to the huge difference in the dielectric constant of water and ice, this paper generated a ten-year surface permafrost change map from 1992 to 2001 based on SSM/I passive microwave remote sensing data, and the results showed that from 1992 to 2001, the annual average frozen area in Tibet showed a downward trend, which also verified the general trend of global warming. According to the comparison between the images generated by the two algorithms and the actual situation, it can be seen that the freeze-thaw discriminant algorithm has better accuracy than the double-index algorithm under the condition of ascending orbit. The results of this study can have important reference value for analyzing the changes of permafrost in Tibet for a long time series and selecting the appropriate soil freeze-thaw discrimination algorithm, and also provide an SSM/I sensor image processing method. © 2023 SPIE.

关键词： Global warming

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