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

共 15613 条记录，以下是741-750 订阅

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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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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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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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Optimization of Micro-Object Identification by Correcting Distorted image Points

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

引用

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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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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An analysis comparing CT and X-ray images for COviD-19 identification employing deep learning and image processing methods 3

An analysis comparing CT and X-ray images for COVID-19 ident...

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3rd International conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Prabu, R. Dhinakar, P. Bharath Institute of Higher Education and Research Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350391565

This study presents a comparative analysis of deep learning approaches for the identification of COviD-19 pneumonia in medical imaging, specifically focusing on CT and X-ray modalities. To aid healthcare professionals in quickly identifying infected individuals during the COviD-19 pandemic, accurate and efficient diagnostic tools are urgently required. In order to identify COviD-19 more quickly and accurately, deep learning algorithms combined with medical imaging data produced results. Computed Tomography (CT) and X-ray imaging are commonly used modalities for assessing lung involvement in COviD-19 patients, each offering unique advantages and challenges. Leveraging deep learning techniques, such as convolutional neural networks (CNNs), this analyzes the performance of models trained on large datasets of labeled CT and X-ray images. Our analysis includes preprocessing methods, model architectures, training procedures, and evaluation metrics to assess the efficacy of these approaches. Furthermore, this discusses the clinical implications, challenges, and ethical considerations associated with integrating deep learning-based diagnostic tools into routine clinical practice. © 2024 IEEE.

关键词： COviD-19

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Drowsiness Detection for Aviation, Maritime and Land Security systems 11

Drowsiness Detection for Aviation, Maritime and Land Securit...

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

作者： Bhati, Riya Jain, Rachna Pandey, Nitin Amity Institute of Information Technology Noida India Amity University Uttar Pradesh India Galgotias University Greater Noida India

ISBN: (纸本)9798350350357

This proposed approach uses image processing and the Python programming language to create an automatic sleepiness detection approach for a person. The objective of this proposed system is to develop an algorithm that can reliably gauge a person's ocular sleepiness under various circumstances by using real-time grayscale photographs. The person will be observed by the system as it scans their face and eyelids for hypovigilance, which is a precursor to tiredness. To properly determine the person's level of sleepiness, the suggested approach will entail three steps: feature selection, eye-pair state detection, and decision making. The algorithm will learn the characteristics that discriminate between the two states by being trained on a collection of photos of both tired and awake drivers and security personnels. To identify sleepiness in persons, facial feature recognition algorithms will also be employed, with an emphasis on the eye pair. images will be taken with a mounted camera. For the system to successfully be integrated into current person assistance systems, it will be measured on how well it is able to identify tiredness. The research's importance stems from its ability to increase safety by identifying person sleepiness, a major contributing factor to accidents and security breaches. © 2024 IEEE.

关键词： Decision making

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Research on Deep Learning-Based SAR image Denoising Algorithm 10

Research on Deep Learning-Based SAR Image Denoising Algorith...

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10th International conference on Dependable systems and Their Applications, DSA 2023

作者： Hu, Shengnan Mao, Ruihan Li, Hua Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9798350304770

Synthetic Aperture Radar (SAR) has the advantages of high penetration, high resolution, all-weather, and all-time, but due to the special imaging mechanism of SAR images, there are a large number of coherent speckle noises in the images. If not processed, it will greatly limit the subsequent use of data. In order to improve the use of SAR images, noise removal processing is needed. In order to choose the appropriate noise removal algorithm, this paper compares the performance of three noise removal algorithms: multiplication model, enhanced Lee-sigma filter, and CNN trained on SAR images (SAR-CNN). The results show that the multiplication model has better noise removal ability and ability to extract effective information. © 2023 IEEE.

关键词： Synthetic aperture radar

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Indian Currency Recognition System Using image processing Techniques 9

Indian Currency Recognition System Using Image Processing Te...

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9th International conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Garkoti, Pravidhi Mishra, Pranshu Rakesh, Nitin Payal Kaur, Mandeep Nand, Parma Sharda University Department of Computer Science Set India

ISBN: (纸本)9789380544441

In today's modern world, an intelligent system is the main requirement for current automation systems like money exchange machines, electronic banking, etc. Such automated currency recognition systems helps in reducing the human efforts to recognize the monetary value of the currency. The currency notes are sometimes blurry or damaged that makes the recognition task difficult. The correct algorithms and features extraction and selection methods are very important to select in the solution. The practical implementation of these systems should be less complex, simple with good efficiency and speed. In this paper, an automated currency system is proposed using image processing techniques. image processing techniques are used to improve the quality of currency image so that correct information can be extracted. Basically currency is scanned for obtaining image, pre-processing operations are performed which include blurring, noise removal etc. Then the boundary detection and cropping is completed so the feature extraction is the last process after which the result is displayed. By these phases the currency recognition is processed and helps us to recognize currency easily. © 2022 Bharati vidyapeeth, New Delhi.

关键词： Feature extraction

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