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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5158 条记录，以下是121-130 订阅

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Optimizing image Classification Using Bag of Features and Support Vector Machines 4

Optimizing Image Classification Using Bag of Features and Su...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mahantesh, K. Navyashree, K.S. Nairy, Devika S. Asha, R. Anshitha, B. Bengaluru India Sjb Institute of Technology Visvesvaraya Technological University Department of Ece Bengaluru India

ISBN: (纸本)9798350352931

image categorization is a fundamental task in computer vision, with applications in domains such as object recognition, medical imaging, and autonomous systems. Traditional approaches frequently fail to balance accuracy, computing efficiency, and scalability, particularly when dealing with big and complex datasets. this work presents a novel picture classification strategy that combines the Bag of Features (BoF) model with Support Vector Machines (SVM). the BoF model describes images by extracting local visual characteristics (such as SIFT, SURF, or ORB) from image patches and quantizing them into visual words to create a histogram representation. SVM, a powerful machine learning classifier, is used to classify these histograms, utilizing its capacity to handle high-dimensional, sparse data. Experiments using common image classification datasets show that the BoF-SVM system greatly outperforms previous methods, resulting in higher classification accuracy and lower processing costs. Furthermore, it has superior generalization to previously unseen data and is more resistant to noise and picture changes. the suggested BoF-SVM system produces promising results for boosting both accuracy and efficiency in image classification tasks, with room for further optimization in more complicated and diversified applications © 2024 IEEE.

关键词： image classification

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Remote Sensing image Captioning Using Hire-MLP 4

Remote Sensing Image Captioning Using Hire-MLP

引用

4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Wei, Yichao Li, Lin Geng, Shengling School of Computer Science Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Qinghai Normal University Academy of Plateau Science and Sustainability Xining China National Qinghai-Tibet Plateau Scientific Data Center Qinghai Sub-center Xining China

ISBN: (纸本)9798350326444

Remote sensing image captioning has been widely applied to traffic management, geographic research, etc. Although the neural network approach has been successfully improving the performance of the Remote sensing image captioning system, Remote sensing image captioning is still facing object identification challenges due to the small size of objects, uneven distribution, and high coupling with the surrounding image background. In this paper, we propose a novel remote sensing image captioning encoder-decoder model Hierarchical rearrangement-Multi-Layer Perceptron (HMLP) whose encoder adapts hierarchical rearrangement-multi-layer perceptron to improve the capability of objection recognition. Extensive experiments have been conducted to testify HMLP by three datasets RSCID, UCM-caption, and NWPU-caption. Results show that HMLP outperforms many image captioning systems in the evaluation metrics BLEU4, METEOR, ROUGE-L, and CIDEr. © 2023 IEEE.

关键词： computer vision

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the Effect of Variance-Based Patch Selection on No-Reference image Quality Assessment 6

The Effect of Variance-Based Patch Selection on No-Reference...

引用

6th international conference on pattern recognition and image Analysis (IPRIA)

作者： Hosseini-Benvidi, S. Farhad Mansouri, Azadeh Kharazmi Univ Dept Elect & Comp Engn Fac Engn Tehran Iran

ISBN: (纸本)9798350314557

the objective of the No-Reference image Quality Assessment (NR-IQA) is to evaluate the perceived image quality subjectively. Since there is no reference image, this is a challenging and unresolved issue. Convolutional neural networks (CNNs) have gained popularity in recent years and have outperformed many traditional techniques in the field of image processing. In order to overcome overfitting, a large percentage of deep learning based IQA methods work with tiny image patches and assess the quality of the entire image based on the average scores of patches. Patch extraction is one of the most crucial elements of CNN-based methods in quality assessment problems. Assuming that visual perception in humans is well suited to extract structural details from a scene, we analyzed the effect of feeding informative and structural patches to the quality framework. In this paper, a method for structural patch extraction is presented, which is based on the variance values of each patch. the obtained results show that the presented method has an acceptable improvement compared to the random patch selection. the proposed model has also performed well in cross-dataset experiments on common distortions, indicating the model's high generalizability. Additionally, the test was run on the flipped images, and the outcomes are satisfactory.

关键词： computer vision image quality assessment Deep learning

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A Hybrid Deep Learning Approach for Automatic Melanoma Diagnosis: Integrating Convolutional Neural Networks with vision Transformers 4

A Hybrid Deep Learning Approach for Automatic Melanoma Diagn...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Priyanka, G. Mahalakshmi, A. Priya, N. Sivaranjani, N. Archana, B. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Hindusthan college of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

the most lethal type of skin lesion is melanoma. the likelihood of survival for melanoma is significantly increased by early detection. Nevertheless, a number of characteristics, such as diminished contrast between the skin and abrasion and visual similarities between non-melanoma and melanoma, make it challenging to identify melanoma with accuracy. the novel melanoma recognition method described in this study consists of three primary phases: preprocessing, segmentation and classification. To maximize image quality and get the data ready for segmentation, the preprocessing step included denoising techniques, grayscale conversion, Gaussian thresholding, and Contrast Limited Adaptive Histogram Equalization (CLAHE). three widely used edge detection algorithms-Canny, Sobel, and Prewitt-were applied to the segmentation process in order to identify lesion boundaries and extract features unique to melanoma. Performance criteria such as Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) were used to assess the accuracy of the segmentation. Based on the comparison analysis, Sobel was found to be the best algorithm for correctly identifying melanoma lesions. the outcomes showed how well the preprocessing methods worked to enhance image quality and how well the segmentation algorithms identified melanoma lesions. this research's subsequent phase will concentrate on classifying images using deep learning models. the segmented images are used to train deep learning model namely Convolutional Neural Networks with vision Transformer. Superior performance is obtained in terms of accuracy, precision, recall, and F1 score when denoising, segmentation, and classification techniques are combined. this highlights the potential of deep learning and image processing fusion in improving skin disease recognition and performance of classification systems. © 2024 IEEE.

关键词： Dermatology

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An Anti-epidemic Delivery Trolley based on YOLOv5 image recognition of Room Numbers and QR Codes 4

An Anti-epidemic Delivery Trolley based on YOLOv5 Image Reco...

引用

4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Jiang, Saibiao Fan, Jianan Sun, Zhijin Deng, Baoru School of Electronic Information Engineering Zhuhai College of Science and Technology Zhuhai China School of Computer Engineering Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798350326444

Since the massive spread of the COVID-19 epidemic in 2020 and the recent spread of influenza, the contactless delivery trolley has met the demands of the government for logistics against epidemics and the needs of logistics companies for deliveries. this paper designs a recognition method based on YOLOv5 (You Only Look Once version 5) for detecting room numbers and QR codes for the needs of anti-epidemic delivery trolleys and implements tasks such as stopping the anti-epidemic trolley at a specified room number and opening different warehouses according to different QR code information. the experiments improved the recognition by filtering the pepper noise through mean filtering. Experimental results show an increase of 2.105 % in the recognition rate of pre-processed QR codes, with an average accuracy rate of 98.9%. the delivery trolley also achieves a stable recognition rate of around 98.81% for room numbers. © 2023 IEEE.

关键词： image recognition

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Object Detection and Segmentation for Scene Understanding via Random Forest 4

Object Detection and Segmentation for Scene Understanding vi...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Chughtai, Bisma Riaz Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665463775

In recent days, object detection become a vast topic in computer vision. Accurate object detection and scene understanding is not an easy task due to illumination, viewpoints, and color intensities. Visual features like color, texture, boundaries, and shape, make an image different from another image. the main goal of scene understanding is to machine work like a human and understand the visual information of an image. Currently, researchers working on novel approaches in this field to make a better understanding of the scene. computer vision portrays a major role in different applications such as health, safety, security surveillance, traffic monitoring, autonomous driving car, object recognition, and tracking. In this paper, we work on a meaningful understanding of an image in the scene. To understand the scene we have done region-based segmentation, and for object detection and labeling, we use the tensor flow algorithm, for geometric features mean of each pixel, harry corner edge detection, and scale-invariant feature transform descriptor. And then object recognition by using random forest. We have performed this experiment on UIUC Sports dataset. the presented model achieved 89.45% recognition accuracy. © 2023 IEEE.

关键词： image segmentation

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A Bone Fracture Detection Using CNN Model 4th

A Bone Fracture Detection Using CNN Model

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Nadia, Afsana Nuruzzaman, Md. Nomani, Khan Md. Shibli Billal, Md. Masum East West Univ Dhaka Bangladesh Hajee Mohammad Danesh Sci & Technol Univ Dinajpur Bangladesh Amer Int Univ Bangladesh Dhaka Bangladesh Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9783031821554;9783031821561

Accurate bone fracture detection can be critical in the medical diagnosis process. this paper proposes a bone fracture detection approach based on deep learning using Convolutional Neural Networks (CNNs) with X-ray images. We have explored three models: one predefined model (MNASNet1) and two custom models (Proposed Model 1 and Proposed Model 2). Model performance is evaluated using metrics such as F1-Score, precision, and accuracy. Results show that the proposed models achieved outstanding performance compared to the MNASNet1 model in most of the metrics. Specifically, Proposed Model 1 achieves the highest overall F1-score, recall, and precision (1.00) and also has the highest validation accuracy (99.89%). Our objective is to develop a custom CNN architecture that demonstrates the potential of our proposed models to enhance fracture detection accuracy and develop a web application to detect bone fractures faster to facilitate more precise clinical assessment and treatment decisions. Doctors can emphasize the benefits and explore collaboration opportunities in a relatable way to integrate our web application into medical practices.

关键词： Bone fracture detection CNN Deep learning X-ray image image classification Biomedical application Medical image processing

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Research on combined preprocessing algorithms for detection of center circle in circular coded target image used in ART 4

Research on combined preprocessing algorithms for detection ...

引用

4th international conference on Signal image processing and Communication, ICSIPC 2024

作者： Wu, Qingdong Wang, Zhiqiang Xu, Yao Yan, Jun Liu, Zhaohui Shandong Luqiao Group Company Ltd. Jinan250014 China College of Transportation Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781510682467

the corded target is an indispensable part in the study of machine vision-based measurement technology, and each coded value corresponds to a different unique value, which makes it be of an important application in image recognition. Since a center circle of the circular coded target can provide precise positioning coordinate, this paper carries out the research on the combined preprocessing algorithm for detecting the center circle of circular coded target images in order to improve the recognition efficiency and accuracy of the center circle of circular coded target image of Autonomous Rail Rapid Transit(ART). Firstly, the color images captured by field experiment are gray-scale processing and smoothed by Gaussian filtering;secondly, Canny edge detection is performed on the images to accurately extract the edge features of the target images. Finally, an improved Hough transform method is used to identify the center circle contour of the extracted edges. the experimental results show that the algorithm not only improves the recognition efficiency, but also ensures the accuracy of recognition. © 2024 SPIE.

关键词： Hough transforms

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Visual Perception Transformer: Robust image understanding on unseen transformations across wide-ranging dataset sizes 24

Visual Perception Transformer: Robust image understanding on...

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4th international conference on AI-ML Systems, AIMLSystems 2024

作者： Nair, Renju C. Gatreddi, Ashish Rao, Madhav Muralidharavn International institute of information technology Bangalore Karnataka Bengaluru India

ISBN: (纸本)9798400711619

vision Transformers (ViT) have seen significant success across diverse computer vision tasks, aided by strategic architectural design and extensive data augmentation techniques that utilize numerous feature maps to capture object transformations. Nonetheless, the existing ViT architecture is inadequate in incorporating information from the full image, especially from the lower layers. Despite this, there has been minimal focus on this crucial aspect. In this research, we introduce the Visual Perception Transformer (VPT) architecture, which addresses the shortcomings of the conventional transformer framework by improving viewpoint invariance and facilitating effective learning from smaller datasets. VPT utilizes the affine transformation properties of capsule networks in conjunction with self-attention mechanisms to effectively integrate information from the entire image. On the imagenet 1K dataset, the VPT achieves an accuracy rate of 88.6% on unchanged images and an accuracy of 71.1% on altered images without employing augmentation during the training phase. Additionally, when the VPT is trained on a reduced dataset, it converges faster and achieves greater accuracy than the traditional ViT. It is envisioned that VPT will be combined with advanced transformer architectures for a range of applications such as Optical Character recognition (OCR) and Medical Imaging [8–10, 15, 20]. © 2024 Copyright held by the owner/author(s).

关键词： Contourlet transform

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Machine vision-Aware Quality Metrics for Compressed image and Video Assessment 27th

Machine Vision-Aware Quality Metrics for Compressed Image a...

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27th international conference on pattern recognition, ICPR 2024

作者： Dremin, Mikhail Kozhemyakov, Konstantin Molodetskikh, Ivan Kirill, Malakhov Artur, Sagitov Vatolin, Dmitriy Lomonosov Moscow State University Moscow Russia Huawei Technologies Co. Ltd. Shenzhen China Shenzhen China

ISBN: (纸本)9783031783463

A main goal in developing video-compression algorithms is to enhance human-perceived visual quality while maintaining file size. But modern video-analysis efforts such as detection and recognition, which are integral to video surveillance and autonomous vehicles, involve so much data that they necessitate machine-vision processing with minimal human intervention. In such cases, the video codec must be optimized for machine vision. this paper explores the effects of compression on detection and recognition algorithms (objects, faces, and license plates) and introduces novel full-reference image/video-quality metrics for each task, tailored to machine vision. Experimental results indicate our proposed metrics correlate better with the machine-vision results for the respective tasks than do existing image/video-quality metrics. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine vision

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