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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81-90 订阅

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Unified image Mastery : An Integrated Approach to image Enhancement, Object Detection and Classification 4

Unified Image Mastery : An Integrated Approach to Image Enha...

引用

4th international conference on Multimedia processing, Communication and Information Technology, MPCIT 2024

作者： Vandana, N. Vidyasagar, K.N. Suguresh Hanamanal, Vachan Arun Pujari, Rahul R. School of Electronics and Communication Engineering REVA University India

ISBN: (纸本)9798350375466

the proposed system integrates YOLOv5 [1] for object detection, CMFNet [2] for image enhancement, and ResNet [3] for image classification into a unified framework for comprehensive image analysis. this integrated approach overcomes limitations of standalone methods, providing a seamless platform for various image analysis tasks. the system demonstrates improved performance in visual quality enhancement, real-time object detection, and accurate image classification across diverse scenarios. the proposed design allows for flexibility and future extensibility, making it adaptable to evolving requirements in computer vision applications. © 2024 IEEE.

关键词： image classification

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Enhancing recyclable waste classification based on deep residual learning 4

Enhancing recyclable waste classification based on deep resi...

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Jiang, Xingsheng School of Physics and Electronic Engineering Northeast Petroleum University Heilongjiang Province Daqing163318 China

ISBN: (纸本)9781510682528

this study aims to leverage advanced deep learning technologies to facilitate high-accuracy waste classification on mobile devices. We developed a deep neural network model based on ResNet-34, specifically designed for the automatic identification and classification of recyclable waste images. Utilizing a residual learning framework, this model enhances the representational capacity of feature maps, effectively captures deeper features, and maintains information flow, thus preventing the common issue of feature degradation during deep network training. Testing on the TrashNet dataset demonstrated that this model surpasses other common convolutional neural network architectures in multiple performance metrics, including accuracy, precision, recall, and F1 score, achieving a classification accuracy of 86.25% and confirming its efficacy in handling complex waste classification tasks. © 2024 SPIE.

关键词： Data modeling Performance modeling Education and training Instrument modeling image classification Feature extraction Mobile devices Deep learning image processing Convolution

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A Novel Transformer-Based Approach to Low-Light image Enhancement 4

A Novel Transformer-Based Approach to Low-Light Image Enhanc...

引用

4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Xia, Guodong Guo, Yongde City University of Macau Faculty of Data Science China

ISBN: (纸本)9798350361643

image quality significantly impacts the effectiveness of computer vision (CV) applications such as autonomous driving, facial recognition, and remote sensing. However, inadequate lighting substantially degrades image quality, resulting in muted colors, reduced visibility, distortion, increased noise, and loss of detail, which can significantly interfere with the performance of these CV-based applications. In response, our research introduces the Fusion Low-Light Former (FLLF) to address the critical need for enhancing images captured in low-light conditions. Our method uses transformer-based techniques and feature fusion to effectively merge low-and-high-level features in degraded images in a hierarchical architecture. Consequently, our FLLF markedly improves low-light image enhancement performance. © 2024 IEEE.

关键词： image enhancement

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Prototype of a System to Assist the Urologist in the recognition of Urinary Bladder Tumors in MRI images 10

Prototype of a System to Assist the Urologist in the Recogni...

引用

10th international conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Martinez, Ares Ulises Juarez Lomas, Jaime Hugo Puebla Talamantes, Jose Felix Serrano ESCOM IPN Dept Syst Engn Computat Mexico City DF Mexico CIDETEC IPN Virtual Real Lab Mexico City DF Mexico

ISBN: (纸本)9798350359374

the proposal aims to develop a prototype system to detect possible bladder tumors. the system Will use image processing techniques, computer vision, segmentation, pattern recognition and machine learning. the prototype Will load images of the urinary bladder (hollow, distensible muscular organ) and obtain a classification of possible bladder tumors.

关键词： urinary bladder image processing artificial intelligence segmentation and machine learning

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English and Spanish Bilinguals' Language processing: An ALE-Based Meta-analysis of Neuroimaging Studies 6th

English and Spanish Bilinguals' Language Processing: An ALE-...

引用

6th Chinese conference on pattern recognition and computer vision (PRCV)

作者： Liu, Linqiao He, Yan Zheng, Aoke Xian Int Studies Univ Coll English Studies Xian 710128 Peoples R China Xian Int Studies Univ Key Lab Artificial Intelligence & Cognit Neurosci Xian 710128 Peoples R China

ISBN: (纸本)9789819985395;9789819985401

the neural mechanisms underlying language processing in English and Spanish bilinguals remain a topic of intense investigation. No neuroimaging meta-analyses about English and Spanish bilinguals' language processing, nevertheless, appeared in the past. In this meta-analysis, Activation Likelihood Estimation (ALE) technique was used to synthesize findings from 13 neuroimaging studies of total 231 Spanish-English or English-Spanish bilinguals. Results showed that there existed 6 peaks in 4 activated brain regions during Spanish processing and 13 peaks in 11 activated brain regions during English processing. In addition, results revealed two distinct peaks of two brain regions commonly activated during language processing across both languages: right insula and left precentral gyrus which may become potential markers for individuals who are bilingual in both English and Spanish. In addition, we found no significant difference in activation between English and Spanish, indicating that these two languages may engage similar neural pathways during language processing.

关键词： Spanish English Bilinguals Meta-analysis ALE

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Role of thermal images in Various Applications of computer vision 4

Role of Thermal Images in Various Applications of Computer V...

引用

4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Gupta, Ravina Jain, Sarika Kumar, Manoj AIIT Amity University Noida India EIS University of Wollongong Dubai United Arab Emirates

ISBN: (纸本)9798350341126

Object detection is an advanced area of image processing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. this paper focuses on various object detection techniques in thermal images. A thermal imaging sensor is a device that creates an image by analyzing temperature differences between different objects in a scene and detecting radiation from those objects. In recent years, many machine learning and deep learning algorithms have been used to recognize objects in thermal images. this study makes a comparison of YOLO, YOLO DarkNet, Retinex algorithm, CNN-based machine learning model Support Vector Machine (SVM), and Gaussian Mixture Model (GMM), Mixer of Gaussian (MoG), Mean Shift Approach, Faster R-CNN and Deep Neural Network along with different datasets. © 2023 IEEE.

关键词： Object detection

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RoboCup SPL instance segmentation based on improved U-Net 4

RoboCup SPL instance segmentation based on improved U-Net

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Zhang, Kaijing Hefei University of Technology No. 485 Danxia road Shushan district Anhui province Hefei China Hefei University of Technology Feicuihu Campus No. 485 Danxia Road Anhui Province Hefei City China

ISBN: (纸本)9781510682528

Semantic segmentation is often used in robots' environment perception and object recognition, which can help robots better understand the surrounding environment and perform correct tasks. In the semantic segmentation of football robots, the semantic segmentation model based on the U-Net model works better in the context of football robot competitions, but there is still room for optimization. In response to the special situational needs of football robots, this paper carries out optimization measures such as data enhancement, adding an attention mechanism, and changing the physical mark extraction model based on the U-Net model, and the overall performance has been improved. On the official data set provided by RoboCup SPL, the accuracy rate finally reached 96.25%. © 2024 SPIE.

关键词： Data modeling image segmentation Machine learning Semantics Performance modeling Education and training Robots Feature extraction Mathematical optimization

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Implementation of a Comparative Study of Convolutional Neural Network Architectures for image Blind Noise Elimination 4th

Implementation of a Comparative Study of Convolutional Neura...

引用

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

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Univ Sidi Mohamed Ben Abdellah Dept Math & Comp Sci M2PA Lab ENS Fes 30030 Morocco

ISBN: (纸本)9783031821523;9783031821530

the field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of image processing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. these methods have demonstrated remarkable performance in image processing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. these findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study.

关键词： Deep Learning image processing image Denoising Convolutinal Neural Networks (CNNs) Blind Noise DnCNN DRNet RIDNET PSNR SSIM

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Research on Automobile Fault Diagnosis Model Based on Neural Network 4

Research on Automobile Fault Diagnosis Model Based on Neural...

引用

4th IEEE international conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Zhai, Shaoqing Wang, Zihan School of Automation Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350359589

With the continuous development and popularization of automotive technology, modern automobiles are equipped with a large number of electronic and mechatronic systems, which makes the diagnosis of automotive faults more complex. the traditional manual detection methods can no longer meet the increasing demand for automobile maintenance. In this study, a neural network-based fault diagnosis algorithm model for automobile external deformation is proposed. In this paper, a dataset containing 8000 images of faulty family minivans and 4000 images of normal cars is constructed. In order to improve the image processing effect, this paper adopts bilateral filtering technique for image denoising to improve the image quality and make it more suitable for subsequent processing. In this paper, ordinary convolutional neural network and DenseNet network models are established respectively. And through model validation, it is found that when DenseNet-264 network is chosen, the model has the lowest error rate and higher accuracy, and the prediction error rate for faulty cars is only 20.6%. © 2024 IEEE.

关键词： Automotive fault diagnosis computer vision Deep learning Neural networks

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Contactless Height Measurement for Infants and Toddlers Based on computer vision 4

Contactless Height Measurement for Infants and Toddlers Base...

引用

4th international conference on Consumer Electronics and computer Engineering, ICCECE 2024

作者： Qiao, Liang Liu, Siyu Li, Jiapeng Zhang, Kejie Wang, Yang Li, Haoyang Liu, Chunyan Changchun College of Electronic Technology Changchun China

ISBN: (纸本)9798350384062

Height is one of the important indicators to evaluate the growth and healthy development of infants and young children, at present, the height measurement of infants and young children at home and abroad is still generally used in the soft ruler or horizontal measuring bed, etc., this traditional measurement method is not only inefficient, but also has a variety of drawbacks. To this end, the system takes the neural network Open Pose as the core, combines image processing, C# and HBuilder, and designs and implements a set of non-contact infant height measurement system based on computer vision technology, which can quickly and accurately measure the height of infants and young children, and has the advantages of non-contact, health and safetyIt can provide parents and doctors with reliable data on the growth and development of infants and young children. © 2024 IEEE.

关键词： computer vision

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