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检索条件"任意字段=2024 International Conference on Computer Vision and Image Processing, CVIP 2024"

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6th international conference on computer vision and image processing, cvip 2021

6th International Conference on Computer Vision and Image Pr...

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6th international conference on computer vision and image processing, cvip 2021

ISBN: (纸本)9783031113482

The proceedings contain 97 papers. The special focus in this conference is on computer vision and image processing. The topics include: Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection;signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures;leaf Segmentation and Counting for Phenotyping of Rosette Plants Using Xception-style U-Net and Watershed Algorithm;fast and Secure Video Encryption Using Divide-and-Conquer and Logistic Tent Infinite Collapse Chaotic Map;visual Localization Using Capsule Networks;detection of Cataract from Fundus images Using Deep Transfer Learning;brain Tumour Segmentation Using Convolution Neural Network;signature Based Authentication: A Multi-label Classification Approach to Detect the Language and Forged Sample in Signature;a Data-Set and a Real-Time Method for Detection of Pointing Gesture from Depth images;Deep-TDRS: An Integrated System for Handwritten Text Detection-Recognition and Conversion to Speech Using Deep Learning;vision HELPER: CNN Based Real Time Navigator for the Visually Impaired;structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated images;low Cost Embedded vision System for Location and Tracking of a Color Object;towards Label-Free Few-Shot Learning: How Far Can We Go?;AB-net: Adult- Baby Net;Polarimetric SAR Classification: Fast Learning with k-Maximum Likelihood Estimator;Leveraging Discriminative Cues for Masked Face Recognition in Post COVID World;pretreatment Identification of Oral Leukoplakia and Oral Erythroplakia Metastasis Using Deep Learning Neural Networks;soft Biometric Based Person Retrieval for Burglary Investigation;a Deep Learning Framework for the Classification of Lung Diseases Using Chest X-Ray images;computer Aided Diagnosis of Autism Spectrum Disorder Based on Thermal Imaging;scene Graph Generation with Geometric Context;deep Color Spaces for Fingerphoto Presentation Attack Detection in Mobile Devices;can

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Design of an Automatic Agricultural Machinery Signage Recognition System Based on computer vision 24

Design of an Automatic Agricultural Machinery Signage Recogn...

引用

3rd international conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Liu, Siqi South China University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798400718120

[Objective] This study aims to design an automatic agricultural machinery identification system based on computer vision, to address the issue of disorganized and chaotic labeling of some agricultural machinery products in the current market, thereby enhancing user recognition efficiency and corporate brand image. [ Methods: ]High-resolution cameras were used to capture images of agricultural machinery under various lighting and weather conditions. image quality was enhanced through techniques such as histogram equalization and adaptive contrast enhancement. Convolutional Neural Networks (CNN) were utilized for feature extraction and classification recognition. The system architecture integrates an image processing module, feature extraction module, classifier module, and user interface to ensure overall system efficiency and user-friendliness. [Conclusion and Results] The results indicate that the system can accurately identify agricultural machinery labels in complex environments, significantly improving recognition accuracy and speed, and meeting the needs for real-time processing. The design method based on visual impact effects has significant application value in the identification of agricultural machinery labels, effectively enhancing the market competitiveness and user satisfaction of agricultural machinery products. © 2024 Copyright held by the owner/author(s).

关键词： Machine vision

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Annotation Tools for computer vision Tasks 17

Annotation Tools for Computer Vision Tasks

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17th international conference on Machine vision, ICMV 2024

作者： Moschidis, Christos Vrochidou, Eleni Papakostas, George A. Department of Informatics Democritus University of Thrace Kavala65404 Greece

ISBN: (纸本)9781510688278

Common computer vision (CV) tasks include image classification, object detection, segmentation, and recognition. To handle such tasks, machine learning (ML) models for image processing require a great amount of annotated training data. While datasets are expanding in size and variety, annotation becomes demanding, since its quality can severely affect the models' performance. Thus, several annotation tools have been developed and designated for specific applications and model requirements. This work aims to provide an overview of the most up-to-date annotation tools for computer vision tasks, including 2D and 3D image data and video, comparatively highlighting their advantages and limitations. The appropriateness of each tool for specific tasks is emphasized, providing a reference map for researchers towards determining the annotation tool best tailored to their needs. Future trends in image annotation are also discussed. © 2025 SPIE.

关键词： image annotation

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2nd international conference on computer vision and image processing, cvip 2017

2nd International Conference on Computer Vision and Image Pr...

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2nd international conference on computer vision and image processing, cvip 2017

ISBN: (纸本)9789811078972

The proceedings contain 64 papers. The special focus in this conference is on. The topics include: A novel robust reversible watermarking technique based on prediction error expansion for medical images;integrated Feature Exploration for Handwritten Devanagari Numeral Recognition;Privacy Preserving for Annular Distribution Density Structure Descriptor in CBIR Using Bit-plane Randomization Encryption;near-Duplicate Video Retrieval Based on Spatiotemporal Pattern Tree;Fingerprint Liveness Detection Using Wavelet-Based Completed LBP Descriptor;silhouette-Based Real-Time Object Detection and Tracking;visual Object Detection for an Autonomous Indoor Robotic System;Engineering the Perception of Recognition Through Interactive Raw Primal Sketch by HNFGS and CNN-MRF;an Efficient Algorithm for Medical image Fusion Using Nonsubsampled Shearlet Transform;effective Denoising with Non-local Means Filter for Reliable Unwrapping of Digital Holographic Interferometric Fringes;a Novel Text Localization Scheme for Camera Captured Document images;video Inpainting Based on Re-weighted Tensor Decomposition;deep Convolutional Neural Network for Person Re-identification: A Comprehensive Review;flexible Threshold Visual Odometry Algorithm Using Fuzzy Logics;fast Single image Learning-Based Super Resolution of Medical images Using a New Analytical Solution for Reconstruction Problem;analyzing ConvNets Depth for Deep Face Recognition;use of High-Speed Photography to Track and Analyze Melt Pool Quality in Selective Laser Sintering;multi-Scale Directional Mask Pattern for Medical image Classification and Retrieval;enhanced Characterness for Text Detection in the Wild;Denoising of Volumetric MR image Using Low-Rank Approximation on Tensor SVD Framework;iris Recognition Through Score-Level Fusion;V-LESS: A Video from Linear Event Summaries;action Recognition from Optical Flow Visualizations.

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Proceedings of international conference on computer vision and image processing 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Balasubramanian Raman Partha Pratim Roy Debashis Sen Sanjeev Kumar

ISBN: (数字)9789811021077

ISBN: (纸本)9789811021060

This edited volume contains technical contributions in the field of computer vision and image processing presented at the First international conference on computer vision and image processing (cvip 2016). The contributions are thematically divided based on their relation to operations at the lower, middle and higher levels of vision systems, and their applications.;The technical contributions in the areas of sensors, acquisition, visualization and enhancement are classified as related to low-level operations. They discuss various modern topics – reconfigurable image system architecture, Scheimpflug camera calibration, real-time autofocusing, climate visualization, tone mapping, super-resolution and image resizing.;The technical contributions in the areas of segmentation and retrieval are classified as related to mid-level operations. They discuss some state-of-the-art techniques – non-rigid image registration, iterative image partitioning, egocentric object detection and video shot boundary detection.;The technical contributions in the areas of classification and retrieval are categorized as related to high-level operations. They discuss some state-of-the-art approaches – extreme learning machines, and target, gesture and action recognition. A non-regularized state preserving extreme learning machine is presented for natural scene classification. An algorithm for human action recognition through dynamic frame warping based on depth cues is given. Target recognition in night vision through convolutional neural network is also presented. Use of convolutional neural network in detecting static hand gesture is also discussed.;Finally, the technical contributions in the areas of surveillance, coding and data security, and biometrics and document processing are considered as applications of computer vision and image processing. They discuss some contemporary applications. A few of them are a system for tackling blind curves, a quick reaction target acquisition and track

关键词： Signal, image and Speech processing image processing and computer vision Biometrics

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2nd international conference on computer vision and image processing, cvip 2017

2nd International Conference on Computer Vision and Image Pr...

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2nd international conference on computer vision and image processing, cvip 2017

ISBN: (纸本)9789811078941

The proceedings contain 64 papers. The special focus in this conference is on . The topics include: A novel robust reversible watermarking technique based on prediction error expansion for medical images;integrated Feature Exploration for Handwritten Devanagari Numeral Recognition;Privacy Preserving for Annular Distribution Density Structure Descriptor in CBIR Using Bit-plane Randomization Encryption;near-Duplicate Video Retrieval Based on Spatiotemporal Pattern Tree;Fingerprint Liveness Detection Using Wavelet-Based Completed LBP Descriptor;silhouette-Based Real-Time Object Detection and Tracking;visual Object Detection for an Autonomous Indoor Robotic System;Engineering the Perception of Recognition Through Interactive Raw Primal Sketch by HNFGS and CNN-MRF;an Efficient Algorithm for Medical image Fusion Using Nonsubsampled Shearlet Transform;effective Denoising with Non-local Means Filter for Reliable Unwrapping of Digital Holographic Interferometric Fringes;a Novel Text Localization Scheme for Camera Captured Document images;video Inpainting Based on Re-weighted Tensor Decomposition;deep Convolutional Neural Network for Person Re-identification: A Comprehensive Review;flexible Threshold Visual Odometry Algorithm Using Fuzzy Logics;fast Single image Learning-Based Super Resolution of Medical images Using a New Analytical Solution for Reconstruction Problem;analyzing ConvNets Depth for Deep Face Recognition;use of High-Speed Photography to Track and Analyze Melt Pool Quality in Selective Laser Sintering;multi-Scale Directional Mask Pattern for Medical image Classification and Retrieval;enhanced Characterness for Text Detection in the Wild;Denoising of Volumetric MR image Using Low-Rank Approximation on Tensor SVD Framework;iris Recognition Through Score-Level Fusion;V-LESS: A Video from Linear Event Summaries;action Recognition from Optical Flow Visualizations.

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Enhancing pedestrian re-identification in low-light environments with multi-scale feature attention extraction network 4

Enhancing pedestrian re-identification in low-light environm...

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4th international conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Li, Zhenghao Xiong, Jiping Li, Jiake College of Physics and Electronic Information Engineering Zhejiang Normal University Zhejiang Hangzhou China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Pedestrian re-identification has witnessed rapid development in recent years, with particular attention given to the challenges posed by complex conditions, including pedestrian re-identification in low-light environments, such as nighttime scenarios. Through our experiments, it was discovered that even with image enhancement in low-light conditions, satisfactory results were not achieved. To address this challenge, this paper introduces a Multi-scale Feature Attention Extraction (MFAE) network. © 2025 SPIE.

关键词： Feature extraction image enhancement Network architectures image segmentation Education and training image processing Data modeling image restoration Lithium Pollution control

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Deep Learning in Medical Imaging: image processing - From Augmenting Accuracy to Enhancing Efficiency 24

Deep Learning in Medical Imaging: Image Processing - From Au...

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7th international conference on Digital Medicine and image processing, DMIP 2024

作者： Lakshmi Vara Prasad, G. Murali, A. Bhaskar Reddy, Y.V. Marturi, Haribabu Paul, Valeru vision Centre for Data Science AI and ML QIS College of Engineering and Technology Andhra Pradesh Ongole India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798400709586

Deep learning has transformed medical imaging by significantly improving accuracy and efficiency in image processing tasks such as disease detection, segmentation, and classification. This paper explores the role of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other advanced architectures in enhancing image interpretation across various medical imaging modalities. By analyzing MRI, CT, and ultrasound data, this study demonstrates the efficacy of deep learning in automating critical diagnostic tasks, reducing human error, and improving patient outcomes. We propose a comprehensive framework for applying deep learning techniques in medical imaging, providing insights into future advancements in the field. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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2024 5th international conference on computer vision, image and Deep Learning, CVIDL 2024

2024 5th International Conference on Computer Vision, Image ...

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5th international conference on computer vision, image and Deep Learning, CVIDL 2024

ISBN: (纸本)9798350373820

The proceedings contain 284 papers. The topics discussed include: spatio-temporal feature coupling of dynamic functional connectivity network for resting-state fMRI brain disease classification;object detection of Visdrone based on attention mechanism and FasterNet;a machine vision-based pan-tilt tracking system;visual odometry with lidar depth enhancement;a model for generating wafer defect maps;jetson nano based YOLOV5 deep learning pavement pothole detection;cartilage repair based on point cloud completion;dynamic temporal resolution transformers;frequency vulnerabilities diversity ensemble for improving adversarial robustness;a method for radar time-frequency image data suppression based on gradient-weighted class activation mapping;and research on the application of ai intelligent design in visual communication design.

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Bone tumor boundary demarcation based on multi-modal image fusion 4

Bone tumor boundary demarcation based on multi-modal image f...

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4th international conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Shang, Ruizhe Dong, Jiaming Wang, Xinhuai School of Electronic Engineering Xidian University Xi’an China Electronic Information Class Xidian University Xi’an China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

The delineation of bone tumor boundaries is a critical issue in the field of medical image segmentation due to the unique positioning of these tumors and the complexity of the associated surgical procedures. In recent years, researchers have made significant strides in determining tumor boundaries through computer version methods. Doctors can achieve precise localization of bone tumors using the U-Net network or the improvements. However, using tumor boundary segmentation models based on single-modal images makes it challenging to capture the complete characteristics of the tumor. In this paper, we propose a multi-modal image fusion network to achieve more accurate segmentation of tumor boundaries. Experimental results show that the accuracy of the segmentation results can be improved by about 6.8% when using fused images for segmentation. Therefore, this image processing process is of great significance for improving the accuracy of bone tumor boundary demarcation and the efficiency of clinical diagnosis. © 2025 SPIE.

关键词： Magnetic resonance imaging

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