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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16443 条记录，以下是1151-1160 订阅

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Leveraging Content Based image Retrieval Using Data Mining for Efficient image Exploration 1

引用

4th International conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Kaur, Jaspreet Gupta, Divya Singh, Amrinder Shah, Syed Hassan Ahmed Department of Computer Science and Engineering Chandigarh University Mohali 140413 India University Institute of Teachers Training and Research ChandigarhUniversity Punjab Mohali India California State University FullertonCA United States

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Deep Learning based Diabetic Retinopathy Detection using image processing 1

Deep Learning based Diabetic Retinopathy Detection using Ima...

引用

1st International conference on Cognitive Computing and Engineering Education, ICCCEE 2023

作者： Rathor, Sandeep Tiwari, Ashish GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350332803

The global prevalence of visual impairment and blindness due to diabetic retinopathy can be significantly reduced through improved diabetes management. Diabetic retinopathy (DR) screening is crucial as it can prevent vision loss. In recent years, personalized screening intervals considering various risk factors have been proposed, showing promising cost-effectiveness ratios. However, limited funding hampers the implementation of widespread screening programs in many countries. A groundbreaking advancement in screening methods is the application of Deep Learning, which revolutionizes the detection and classification of diabetic retinopathy while reducing costs. By automating the analysis of retinal images, Deep Learning techniques enable efficient and accurate identification of DR, enabling timely intervention and treatment. Moreover, emerging research suggests that retinal imaging can not only detect existing diabetic retinopathy but also identify individuals at risk, expanding the scope of screening beyond the detection of sight-Threatening diseases. This paves the way for proactive measures to prevent or mitigate the progression of DR. Therefore, this research aims to develop a Deep Learning-based system for diabetic retinopathy detection using image processing techniques. By leveraging the power of Deep Learning algorithms and advanced image analysis, this study seeks to improve the accuracy, efficiency, and accessibility of DR screening, ultimately enhancing patient outcomes and reducing the burden on healthcare systems. © 2023 IEEE.

关键词： image analysis

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Application of Various Novel Computer image processing (CIP) algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： xian Du Shanghai Institute of Visual Arts Shanghai China

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm.

关键词： Training image coding Art image analysis Translation image edge detection Digital images Semantics Media Feature extraction

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Deep Learning Based Crack Detection Method for Bridges

Deep Learning Based Crack Detection Method for Bridges

引用

Electronics, Energy systems and Power Engineering (EESPE), IEEE International conference on

作者： Qinxue Wu Ruida Han Chang'an University Xi'an China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

In this paper, we focus on the problem of bridge crack detection. In view of the characteristics of bridge cracks, such as the existence of multiple scales and shapes, and the interference of complex backgrounds and different illumination conditions on the surface of bridges, we select the bridge crack images covering different scales, types, and illumination conditions to construct a dataset, and then carry out the image enhancement processes, such as rotating, randomly erasing, and adjusting the brightness and contrast of the cracks. Three deep learning algorithms, ResNet18, MobileNetV2 and EfficientNetB0, are selected for the experiments, and the performance of the models is evaluated by comparing the F1 scores, recall rates, confusion matrices, and observed loss curves. The results show that the proposed data processing and deep learning modeling strategy is effective in bridge crack detection, and the classification effect reaches a high level, providing a feasible method for bridge crack detection.

关键词： Bridges Deep learning Power engineering Shape Lighting Interference Robustness Surface cracks image enhancement Surface treatment

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Soccer Player Tracking Using UAV imagery: A Comparative Study of Yolo and Traditional image processing algorithms

Soccer Player Tracking Using UAV Imagery: A Comparative Stud...

引用

International conference on Unmanned Aircraft systems (ICUAS)

作者： Felipe dos Anjos Rezende Thayron Miranda Hudson Pedro Augusto Fialho Silva Werikson Frederiko de Oliveira Alves André Luis Carvalho Mendes Alexandre Santos Brandão Nucleus of Specialization in Robotics Department of Electrical Engineering Universidade Federal de Viçosa Viçosa MG Brazil

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

Player tracking is a useful tool for tactical analysis and performance evaluation in soccer, providing valuable insights into player movements and team dynamics. This project investigates the feasibility of tracking players using UAVcaptured imagery, employing both YOLO and traditional image processing algorithms (TIPA). Initial validation focuses on robot soccer players due to their predictable and controllable movements. The comparative analysis considers processing time, computational cost, adaptability to environmental changes, sensitivity to lighting variations, ability to handle dynamic conditions, tracking accuracy, and real-time performance. Results indicate that, under equivalent hardware and preparation time conditions, YOLO achieves performance comparable to traditional techniques. Nonetheless, the selection of the most suitable approach should be guided by task-specific demands, available computational resources, and the time allocated for system development and deployment.

关键词： YOLO Performance evaluation Sensitivity Tracking image processing Lighting Robot sensing systems Prediction algorithms Real-time systems Sports

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YOLOv8 image processing for Evaluation of Stability algorithms Based on Neural Networks: A Sports Use Case 8th

YOLOv8 Image Processing for Evaluation of Stability Algorith...

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8th International conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Rahman, Md. Habibur Mohiul Islam, A.S.M. Hasan, Abdullah Ibnah Uddin, Mahtab Ahmed, Ashek Miazee, Asif Ahammad Hossain, Yamin Department of Mathematics University of Chittagong Chittagong Bangladesh Dhaka College Dhaka Bangladesh Institute of Natural Sciences United International University Dhaka Bangladesh Department of Computer Science Maharishi International University FairfieldIA United States Department of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9789819777099

Sports image classification is a complex problem with many different sports involved. It has subpar detection performance and challenges with feature recognition. The issue of classifying 110 different sports image categories is tackled by this study using four pre-trained models: Residual Network-50, EfficientNet-B7, Densely Connected Convolutional Network-121 (DenseNet-121), and You Only Look Once version 8 (YOLOv8). This dataset, which includes 12,500 sports images, provides a solid test base for this research. Comparing their results, ResNet-50 shows excellent performance on the training set, with an accuracy of 94.90 and 91.75% on the validation set. The EfficientNet-B7 model has an estimated accuracy of 65.48% in inference and 41.49% in training. Its underperformance may be due to its extraordinary performance and inability to represent specific gaming images accurately during the image segmentation task. DenseNet-121 performs 75.80% accuracy on the training set and 88.24% on the validation set. It does better than EfficientNet-B7 for capturing features from reflection images. The Geolovin-8 model performed well and averaged 94.90% accuracy on the training set and 97.87% on the validation set in most cases. These results demonstrate that Geolovin-8 has good image recognition and classification performance in the game. This article compares the performance of complex image classification algorithms concerning the same confidential image classification capabilities so that the required information is available. Knowing the advantages and disadvantages of these qualitative features may be necessary to understand the challenges of latent image classification and may suggest future research efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image classification

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Cotton Plant Disease Detection and Prevention Using Deep Learning and image processing 11

Cotton Plant Disease Detection and Prevention Using Deep Lea...

引用

11th International conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kesani, Ruthvija Sunkavalli, Jaya Prakash Shaik, Mahamamad Rafi Yadala, Kusuma Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Vijayawada India

ISBN: (纸本)9798350350357

One of the most important aspects of the agricultural economy is the production of cotton, which is threatened by diseases that lower crop quality and yield. Conventional techniques for diagnosing diseases are frequently subjective and labor-intensive. This paper presents a novel method for the automatic detection and prevention of cotton plant diseases that makes use of deep learning techniques. A convolutional neural network (CNN) model is trained using a dataset that includes various photos of both healthy and sick cotton plants. The suggested model offers a dependable and time-efficient solution by exhibiting high accuracy in differentiating between different diseases. Moreover, proactive disease detection is made possible by the integration of real-time monitoring systems, such as drones fitted with high-resolution cameras. Early detection lessens the need for broadspectrum antibiotics by enabling the prompt application of preventive measures, such as targeted therapies. Finally, we conduct a comprehensive computational analysis of eight cutting-edge object detection algorithms on the cotton plant dataset to identify diseases on the leaves and seven cutting-edge classification algorithms on the cotton plant datasets to determine if a leaf has a disease or not. computed results indicate that it has a high degree of object detection accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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A single image dehazing algorithm based on black and bright channel fusion 7

A single image dehazing algorithm based on black and bright ...

引用

7th International conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Fu, Qiang Xu, Tao Changsha University of Science and Technology School of Electrical and Information Engineering Changsha China Changsha University of Science and Technology College of Energy and Power Engineering Changsha China

ISBN: (纸本)9781665478571

To solve the problem of inaccurate estimation of atmospheric light and transmission, this paper proposes a single image dehazing algorithm based on the fusion of black and bright channel. First, finding the block with the largest mean-gradient ratio in the image and the point closest to the maximum pixel value in the block, then taking the pixel value of this point as the atmospheric light. Secondly, the black channel map of haze image is obtained through a minimum filter and a corrosion operation, and the black channel transmission is calculated. Then, maximum filtering twice for haze image to obtain the bright channel map, and the transmission of bright channel map is calculated. The transmission of bright and black channel is fused to obtain the total transmission and then it is handled by guidedfiltering. Finally, the dehazing image is restored by the atmospheric scattering model. The experimental results show that our method can not only obtain more accurate value of atmospheric light and transmission, but also get a good dehazing result. The evaluation indicators of dehazing images are better than other two dehazing algorithms. © 2022 IEEE.

关键词： Demulsification

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Performance Analysis of Low-Cost Solid-State LiDAR for Building Elements Point-Cloud Mapping 2nd

Performance Analysis of Low-Cost Solid-State LiDAR for Build...

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2nd International conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Lo, Ying Cao, Yun Zhou, xingyang Zhang, Cheng Chen, Min Department of Civil Engineering Xi’an Jiaotong-Liverpool University Suzhou China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9789819639489

Accurate point cloud data is vital for the up-to-date representation of buildings. The use of Terrestrial Laser Scanners (TLS) has been prevalent for generating precise point clouds in the built environment. However, despite the development of scan planning algorithms to address issues such as occlusions and varying point densities, the significant costs and extensive time required for data acquisition and processing—often worsen by the lack of existing plans for scan planning—limit the practicality and cost-effectiveness of TLS. This necessitates the investigation of alternative methods for point cloud data collection. This study assesses the capabilities and limitations of low-cost solid-state LiDAR sensors, specifically the Livox Mid-360, in mapping the built environment. The sensor’s performance was benchmarked against TLS and onsite measurements, testing its mapping accuracy over distances ranging from 2 m to 8 m and on different materials including steel, wood, plastic, and concrete. The results demonstrate that the sensor achieves sub-centimeter accuracy within a 2 m range, with the highest accuracy on wood and the least on steel. These results validate the efficacy of low-cost solid-state LiDAR sensors for building modeling requiring centimeter-level accuracy, offering a more affordable and accessible solution for updating Building Information Modelling (BIM) model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mapping

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Application of K-nearest Neighbor Algorithm Based on Eye Movement Data in the Analysis of Cognitive Decline in Alzheimer's Disease 7

Application of K-nearest Neighbor Algorithm Based on Eye Mov...

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7th International conference on Vision, image and Signal processing, ICVISP 2023

作者： Zhang, Yinjuan Fei, Yue Nursing department Air Force Medical Univerity Shaanxi Xi'an China Nursing department Shaanxi University of Chinese Medicine Shaanxi Xi'an China School of Computer Science and Engineering Xi'an Technological University Shaanxi Xi'an China

ISBN: (纸本)9781839539848

Alzheimer’s disease, a progressive neurodegenerative condition, demands early detection to mitigate its progression. Eye-tracking technology has become a significant research tool due to its non-invasive nature and sensitivity to cognitive changes. To assess cognitive decline in Alzheimer’s patients, an advanced K-nearest neighbor algorithm is applied to analyze eye movement data. This improved algorithm enhances classification accuracy by optimizing feature selection and parameters. The experimental results show that the improved model has better performance indexes, its F1 value reaches 0.978, and its recall value reaches 0.962;it has better convergence performance, and its training efficiency is better, which can save time and computational resources more effectively;it has higher accuracy in practical use, and it is less disturbed by the size of features, and the average accuracy reaches 95.67%, compared with that of the K-Nearest Neighbor algorithm. 95.67%, which is 13.64% and 24.11% higher than the K-mean algorithm and the density-based spatial clustering algorithm for noise applications, respectively. The improved algorithms can better analyze the cognitive decline of patients and assist physicians in disputing the patients' disease conditions, thus helping patients to carry out accurate treatment and intervention, which can provide a powerful help for the pre-diagnosis of Alzheimer's disease. © The Institution of Engineering & Technology 2023.

关键词： K-means clustering

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