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

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Improved Surface Defect Classification from a Simple Convolutional Neural Network by image Preprocessing and Data Augmentation 1

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10th International Work-conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Lopez de la Rosa, Francisco Moreno-Salvador, Lucia Gomez-Sirvent, Jose L. Morales, Rafael Sanchez-Reolid, Roberto Fernandez-Caballero, Antonio Univ Castilla La Mancha Inst Invest Informat Albacete Calle Invest 2 Albacete Spain Univ Castilla La Mancha Dept Ingn Elect Elect Automat & Comunicac Ave Espana S-N Albacete Spain Univ Castilla La Mancha Dept Sistemas Informat Ave Espana S-N Albacete Spain

ISBN: (数字)9783031611377

ISBN: (纸本)9783031611360;9783031611377

Convolutional neural networks (CNNs) play an important role in an increasing number of image processing tasks. There is an obvious demand to improve their classification performance and efficiency. Current research in this area tends to focus on developing increasingly complex models and algorithms to achieve this end. However, research into computer vision techniques and data augmentation tends to be neglected. This paper demonstrates that even a very simple CNN model achieves high performance in surface defect classification on the NEU dataset thanks to image preprocessing and data augmentation. The initial F1-score of 0.9646 without image preprocessing increases to 0.9727 when preprocessing is carried out. The simple CNN then achieves an F1-score of 0.9854 after data augmentation.

关键词： Surface defect classification image preprocessing data augmentation convolutional neural network

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Unsupervised Learning for Land Cover Mapping of Casablanca Using Multispectral Imaging

Unsupervised Learning for Land Cover Mapping of Casablanca U...

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2024 ASU International conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Ouchra, Hafsa Belangour, Abdessamad Erraissi, Allae Hassan Ii University Laboratory of Information Technology and Modeling Ltim Faculty of Sciences Ben m'Sik Casablanca Morocco Chouaib Doukkali University Polydisciplinary Faculty of Sidi Bennour El Jadida Morocco

ISBN: (纸本)9798350372229

Precise and current land use data hold immense significance in facilitating efficient urban planning and appropriate environmental oversight. This paper proposes an approach to the unsupervised classification of Casablanca's land use using the Google Earth Engine (GEE) platform. The study relies on multispectral satellite imagery, in particular data from Landsat satellites, to extract meaningful land use categories without resorting to manual labeling. The operational process includes data collection, pre-processing, unsupervised clustering, and graphical display of results. By applying the k-means and Lvq clustering algorithms, the urban area is split into distinct groups, each representing a specific land use class. The resulting land use map provides valuable data on Casablanca's urban fabric, highlighting wooded areas, agricultural land, built infrastructure, water bodies, and barren land. This automated approach demonstrates GEE's potential as a powerful tool for analyzing land use, enabling informed, data-driven decisions on urban development and environmental monitoring. The methodology outlined can serve as a reference for similar research in other regions, helping to advance remote sensing and geospatial analysis techniques in urban and environmental studies. The effectiveness of these two algorithms is assessed in terms of overall accuracy and kappa coefficient. The k-means algorithm showed moderate accuracy, while the Lvq algorithm showed the least satisfactory results. © 2024 IEEE.

关键词： Clustering Google Earth Engine Land cover machine learning satellite image Unsupervised classification urban planning

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Comparison of DAT with DCT in a Viewpoint of Current image processing and Analysis Trends 12

Comparison of DAT with DCT in a Viewpoint of Current Image P...

引用

12th IEEE International Scientific Technical conference on Dependable systems, Services and Technologies (DESSERT) - Dependable and Trustworthy AI, IoE, Big Data Analytics for a Safe and Secure World

作者： Makarichev, Viktor Lukin, Vladimir Brysina, Iryna Natl Aerosp Univ KhAI Dept Comp Syst Networks & Cybersecur Kharkiv Ukraine Natl Aerosp Univ KhAI Dept Informat Commun Technol Kharkiv Ukraine Natl Aerosp Univ KhAI Dept Higher Math & Syst Anal Kharkiv Ukraine

ISBN: (纸本)9798350333046

Digital images are a type of data that has many applications. There are many constructive tools for their analysis and processing. In particular, various discrete transforms are used in order to get useful data features. Here, discrete atomic transform (DAT) and discrete cosine transform (DCT), which are discrete data transforms based, respectively, on atomic and trigonometric functions, are compared in a viewpoint of current image processing and analysis trends. Nowadays, due to a combination of challenges, it is of particular importance to develop such algorithms that provide data compression and protection features in combination with artificial intelligence oriented format. For different reasons, in particular functional properties, application of such non-classic tools as atomic functions to solving this problem seems to be promising. Trigonometric functions are widely used in image processing and can be de-facto considered as a standard. In this research, we provide a comprehensive comparison of DAT and DCT using different criteria, as well as discuss their strengths and weaknesses in the context of the problem considered.

关键词： image processing image representation discrete atomic transform discrete atomic compression discrete cosine transform

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Optimization of Micro-Object Identification by Correcting Distorted image Points

Optimization of Micro-Object Identification by Correcting Di...

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2023 International Russian Smart Industry conference, SmartIndustryCon 2023

作者： Jumanov, Isroil I. Safarov, Rustam A. Djumanov, Olimjon I. Samarkand State University Intelligent Systems and Computer Technologies Samarkand Uzbekistan

ISBN: (纸本)9781665464291

A methodology for optimizing the identification, recognition and classification of micro-objects has been implemented using dynamic models for transforming the original image, synthesizing mechanisms for extracting redundant information structures and using histological, morphological, and texture characteristics of images. Implemented mechanisms for detecting, highlighting and correcting the values of distorted points by embedding new data, segmenting the contour, reducing redundant fragments, and setting variables. Identification algorithms have been developed that take into account the correlations of a set of points, the dynamics of their change, matrices of coordinates of points, deformation of segments and the selection of homogeneous areas of images. A comparative analysis of the effectiveness of the mechanisms for pre-processing images, recognition and classification based on testing pictures of medical diagnostics, and pollen grains in solving problems of selection and seed production of grain was carried out. Modified component schemes, and learning algorithms for a three-layer neural network. A generalized mechanism of identification is implemented in case of non-linearity of connections "inputs and outputs"under conditions of a priori insufficiency and uncertainty. Five types of neural network software modules were tested with supervised and unsupervised learning algorithms based on network learning with modified samples with vector quantization, clustering, and identification with "sliding windows". The efficiency of identification with filtrations in the presence of "noise", approximation of the reference points of the curve of the contour of images of micro-objects has been studied. © 2023 IEEE.

关键词： Dynamic models

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

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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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International conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024

International Conference on Artificial Intelligence, Compute...

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2024 International conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024

ISBN: (纸本)9798350394528

The proceedings contain 158 papers. The topics discussed include: duct inspection and monitoring robot;deep learning-based approaches for preventing and predicting wild animals disappearance: a review;classification and tracking of items on a moving conveyor belt using convolutional networks and image processing;critical analysis of the 220/110/20 kV Sardanesti power substation from Romania in the context of identification elements of instability and insecurity;machine learning based collaborative prediction of SSD failures in the cloud;the impact of explainable ai on low-accuracy models: a practical approach with movie genre prediction;utilizing transfer learning-based algorithms for breast ultrasound data in multi-instance classification;predictive maintenance model-based on multi-stage neural network systems for wind turbines;and using teaching learning-based optimization with convolutional neural network to detect pneumonia based on chest X-Ray images.

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Research on Automatic Screening Algorithm of Electric Power image Recognition Data Based on Artificial Intelligence 2

Research on Automatic Screening Algorithm of Electric Power ...

引用

2nd IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2022

作者： Cao, Gang Liu, Yaohong Chen, Liang Xu, Tong Yu, Ting Yang, Sixu Zhang, Yu Hu, Yi Meishan Power Supply Company of State Grid Sichuan Electric Power Company Sichuan Meishan620000 China

ISBN: (纸本)9781665442763

In this paper, artificial intelligence digital image processing technology is used to process power images to form an automatic power image screening system. This method can replace the traditional artificial power image recognition method. This paper studies the specific methods of power image processing and the feasibility of applying them to power image recognition systems. It studies various algorithms for image recognition data automatic screening, feature extraction and pattern recognition, and studies feature extraction for power images. As well as a variety of algorithms for model recognition, we have studied the monitoring and recognition algorithms for power images based on SIFT and RANSAC methods. The selected template image and the key points of the test image are roughly matched. RANSAC removes the wrong matching points. Affine transformation, positioning the set template image in the test image. © 2022 IEEE.

关键词： image recognition

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Medical image Segmentation Techniques: Advances and Challenges

Medical Image Segmentation Techniques: Advances and Challeng...

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International image processing, Applications and systems conference (IPAS)

作者： Hela Boulehmi Rania Linda Filali ISET de Bizerte DGET Bizerta Tunisia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging.

关键词： Deep learning image segmentation Thresholding (Imaging) Machine learning algorithms image edge detection Focusing Feature extraction Convolutional neural networks Medical diagnostic imaging Active contours

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Deep Learning based Diabetic Retinopathy Detection using image processing 1

Deep Learning based Diabetic Retinopathy Detection using Ima...

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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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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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