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

共 13626 条记录，以下是251-260 订阅

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A Low-Cost Deployment Case Study for Industrial IoT Data Collection and processing 4

A Low-Cost Deployment Case Study for Industrial IoT Data Col...

引用

4th International conference on Intelligent Technology and Embedded systems, ICITES 2024

作者： Zhang, Jiajun Hu, Biao China Agricultural University College of Engineering Beijing China

ISBN: (纸本)9798331506292

Despite the widespread implementation of SCADA systems in factories for centralized data management, their functionality is restricted to devices equipped with sensors. Manual readings are still prevalent for critical instruments like temperature and pressure gauges, lacking interfaces, leading to inefficiencies and errors. Upgrading production line sensors with IoT-enabled instruments is costly and disruptive. To tackle this, we propose a cost-effective data acquisition system that leverages edge acquisition devices and deep learning algorithms without altering the existing equipment and processes. Our solution involves installing new sensors or cameras in critical areas and employing image recognition algorithms alongside indirect measurement techniques. This approach enhances the real-time accuracy of data collection, significantly boosts the overall efficiency of the production process, and reduces operating costs. © 2024 IEEE.

关键词： Semantic Segmentation

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Parallel Dual Broad Learning Surrogate Assisted Semi-supervised Kernel Multi-objective Evolutionary Rough Fuzzy Clustering Algorithm for image Segmentation 6

Parallel Dual Broad Learning Surrogate Assisted Semi-supervi...

引用

6th International conference on Natural Language processing (ICNLP)

作者： Du, Weilin Zhao, Feng Xian Univ Posts & Telecommun Sch Commun & Informat Engn Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

To improve the segmentation performance of multi-objective evolutionary clustering algorithms, this paper proposes a parallel dual broad learning surrogate assisted semi-supervised kernel multi-objective evolutionary rough fuzzy clustering algorithm (PDBLS-SKMRFC) for image segmentation. First, the algorithm constructs a parallel dual broad learning surrogate assisted multi-objective evolutionary framework. It uses two broad learning systems as classification and regression surrogate models to evaluate the population in parallel manner. In the framework, a multi-population division strategy guided by dual broad learning system, a dominant individual crossover strategy, a sub-population mutation strategy, and a hierarchical environment selection strategy are designed to obtain more excellent offspring populations. Then, a semi-supervised kernel rough fuzzy intra-class compactness function is constructed, which uses a few labels and pseudo-labels as supervision information to improve the image segmentation performance, and evaluates the clustering quality together with the kernel separability function. Finally, a semi-supervised kernel rough fuzzy clustering validity index is designed to select the optimal solution from the final non-dominated solution set for image segmentation. Experimental results on color images show the effectiveness of the proposed algorithm.

关键词： image segmentation rough fuzzy clustering multi-objective evolutionary optimization surrogate assisted optimization semi-supervised learning

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Optimization of image Compression and Decompression Performance Based on Genetic Algorithm

Optimization of Image Compression and Decompression Performa...

引用

2024 International conference on Interactive Intelligent systems and Techniques, IIST 2024

作者： Li, Weishi Yin, Chun Fujian Polytechnic of Information Technology Fuzhou China

ISBN: (纸本)9798350374421

As one of the main carriers of information transmission and storage, image data need to be compressed in some application scenarios. Traditional image compression methods, although reducing the size of image files to a large extent, are still difficult to meet the performance requirements of massive, high-speed image data compression processing. In this paper, an image compression and decompression performance amelioration method based on genetic algorithm is proposed, aiming to ameliorate the efficiency and quality of image compression, while reducing the time and computational complexity of decompression. This paper first introduces the background and current status of image compression technology, and analyzes the progress of existing image compression research. Then, the basic principles of genetic algorithm and its application in image compression are introduced in detail. On this basis, an image compression algorithm based on genetic algorithm is proposed. The algorithm ameliorates the parameter settings in the image compression process through genetic algorithm to achieve efficient compression of images. At the same time, combined with the image decompression process, the decompression algorithm is ameliorated to reduce the consumption of computational resources. The research in this paper provides new ideas and methods for the performance amelioration of image compression and decompression technology, which has important theoretical and practical value. © 2024 IEEE.

关键词： Genetic algorithms

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Intelligent Medical Assistant - AI Medical image Assistance Technology and Its Application in Cancer Diagnosis and Treatment Decision

Intelligent Medical Assistant - AI Medical Image Assistance ...

引用

2024 International conference on Optical Communication and Optoelectronic Technology, OCOT 2024

作者： He, Yuli The First School of Clinical Medicine Lanzhou University Lanzhou China

ISBN: (纸本)9781510683266

Artificial intelligence (AI) is a computer technology that has a human way of thinking and can imitate the human brain. The integration of AI into medical imaging has revolutionized diagnosis and treatment, providing significant advances in diagnostic accuracy, treatment planning, and patient care. In the context of widespread global cancer challenges, this study will focus on the effectiveness of AI technology for early detection in the field of imaging and histological image analysis, and review strategies for using image processing technology to detect tumors. We discussed the latest AI-driven technologies, including deep learning algorithms, computer-aided detection systems (CAD), and computer-aided diagnosis (CADx), which excel at processing large data sets to produce accurate and valid results in cancer detection, helping imaging physicians accurately identify cancer, and enhancing early cancer diagnosis. And then improve the patient's prognosis through therapeutic intervention. © 2024 SPIE.

关键词： Deep learning

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Convolutional Transformer-Based image Compression 26

Convolutional Transformer-Based Image Compression

引用

26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Arezki, Bouzid Feng, Fangchen Mokraoui, Anissa University Sorbonne Paris Nord L2TI Laboratory Villetaneuse France

ISBN: (纸本)9798350304985

In this paper, we present a novel transformer-based architecture for end-to-end image compression. Our architecture incorporates blocks that effectively capture local dependencies between tokens, eliminating the need for positional encoding by integrating convolutional operations within the multi-head attention mechanism. We demonstrate through experiments that our proposed framework surpasses state-of-the-art CNN-based architectures in terms of the trade-off between bit-rate and distortion and achieves comparable results to transformer-based methods while maintaining lower computational complexity. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： image compression

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Machine Learning Based Skin Disease Detection Using Semi-Automatic image Segmentation

Machine Learning Based Skin Disease Detection Using Semi-Aut...

引用

2024 International conference on Smart systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Sathya, J. Kalaivani, A. New Horizon College of Engineering Department of Mca Bengaluru India

ISBN: (纸本)9798350378177

The human skin is a remarkable structure, susceptible to numerous known and unknown diseases. Thus, diagnosing skin conditions is one of the most uncertain and complex areas in medical science, making clinical image analysis for skin diseases a formidable task. Moreover, diagnosing skin conditions manually by medical professionals is not only time-consuming but also subjective. This paper presents an approach for diagnosing and categorizing skin diseases utilizing machine learning classification techniques. This approach is based on a popular Semi-automatic image segmentation algorithm known as Interactive Superpixel Segmentation. It combines manual user input with automated algorithms to partition images into meaningful regions. Morphological operations are used in this system to remove hair in image processing. Median filtering technique is employed for noise removal. Computer-based methods will be employed to analyze, process, and classify image data, taking into account different attributes of the photos under examination. To start with, skin photographs go through filtering to eliminate unwanted noise, followed by processing to enhance the overall quality of the image. Gray-Level Co-occurrence Matrix (GLCM) is implemented to extract the features from the skin disease images. Following feature extraction, the image can be classified using the K-Nearest Neighbors (KNN) algorithm, ultimately generating an evaluative report as the output. This paper summarizes the basic workflow for interactive superpixel segmentation in machine learning. Medical image dataset is taken from different websites to predict the skin diseases. This application is more efficient and producing more accuracy results comparing to others. © 2024 IEEE.

关键词： Median filters

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Faster image Deblurring for Unmanned Aerial Vehicles 2

Faster Image Deblurring for Unmanned Aerial Vehicles

引用

2nd International conference on Unmanned Vehicle systems (UVS-Oman)

作者： Sineglazov, Viktor Lesohorskyi, Kyrylo Chumachenko, Olena Natl Aviat Univ Dept Aeronavigat Elect & Telecommun Kiev Ukraine Natl Tech Univ Ukraine Dept Artificial Intelligence IASA Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine

ISBN: (纸本)9798350372557

This work is devoted to the development of a novel deep learning encoder-decoder algorithm for real-time noise and blur elimination in video frames, received from UAV. This work improves on existing algorithms by providing a more flexible blind deblurring solution than existing kernel-based methods. The proposed method can be applied to both improve the drone operator's capabilities and to improve the performance of autonomous image processing tasks, such as object identification and visual navigation systems. Different types of blur as well as possible types of noise are presented. A brief overview of existing methods is provided. The problem of frame alignment due to the object's movement and associated noise is considered. Existing deblurring and image restoration methods are reviewed, including state-of-the-art. Their limitations are highlighted. To solve the limitations a method based on a fully convolutional encoder-decoder network with residual connections is presented. Dataset generation and training procedures are discussed. The approach is then compared to existing state-of-the-art deep learning methods. The proposed method enables up to 9 times faster blind image restoration with comparable quality in comparison to existing state-of-the-art image restoration methods.

关键词： convolutional neural networks decoder-encoder blind deblurring unmanned aerial vehicles blur kernels

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Multi-platform Framework for Pattern and image processing 2nd

Multi-platform Framework for Pattern and Image Processing

引用

3rd International conference on Digital Technologies and Applications

作者： Pedro Lura-Murillo, Ramiro Torres-Cruz, Fred Huanca Suaquita, Jhon Richard Marleny Auquitias-Condori, Gladys Paul Herrera-Urtiaga, Alain Univ Nacl Altiplano Puno POB 291 Puno Peru Univ Nacl Juliaca Puno Peru

ISBN: (纸本)9783031298592;9783031298608

This article describes the creation and development of a Web Framework for the processing of images and Patterns based on the power of the HTML5 language and the object-oriented capabilities of the Javascript language, as well as the importance of its functionality on any operating system, thereby improving and simplifying its use. The object-oriented implementation will make it easier to access image variables and characteristics (width, height, color depth and bitmap), the calculation algorithms and matrix processing have been modified to perform convolution and color discretization processes, as well as the use of Gaussian filters and the Sobel, Cannis, Roberts Operator. Leaving the data ready to search for patterns and generate color histograms. As a result, a clear, compact implementation was obtained. to be able to derive and run on any browser that supports HTML5 technology and, more importantly, on any operating system. We provide greater flexibility and portability than other CGI frameworks based on C++ and Python.

关键词： CGI Framework image processing Web App Web Technology

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Automated Detection and Analysis of Road Cracks and Pothole Depths using Computer Vision and Depth Imaging 15

Automated Detection and Analysis of Road Cracks and Pothole ...

引用

15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Makkena, Manohar Lingamallu, Geyani Digavalli, Veda Harshitha Gudupalli, Vamshidhar Reddy Manikandan, V.M. Panchikkil, Shaiju Srm University-AP Andhra Pradesh India

ISBN: (纸本)9798350370249

Maintaining road infrastructure is essential to effective transportation systems and public safety. This research provides a new method for pothole depth estimation and automatic road crack detection using computer vision techniques. Our method utilizes convolutional neural networks (CNNs) for classifying road images into 'With Crack' or 'Without Crack' categories with high accuracy. Additionally, we employ image processing algorithms to detect and highlight cracks, providing insights into their lengths and percentages. Furthermore, we introduce a monocular depth estimation model to assess pothole depths, aiding in prioritizing road repair efforts. Experimental results demonstrate the effectiveness of our approach in accurately identifying road defects and estimating their severity. This research contributes to the advancement of intelligent infrastructure management systems, enabling proactive maintenance and ensuring safer roads for communities. © 2024 IEEE.

关键词： CNN Heat Map image Analysis image Classification Monocular depth estimation

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Identification of Polycystic Ovary Syndrome in ultrasound images of Ovaries using Distinct Threshold based image Segmentation

Identification of Polycystic Ovary Syndrome in ultrasound im...

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2023 International conference on Advancement in Computation and Computer Technologies, InCACCT 2023

作者： Poorani, B. Khilar, Rashmita SIMATS University Saveetha School of Engineering Department of Computer Science Engineering Thandalam Chennai India

ISBN: (纸本)9798350396485

Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder among women of reproductive age and it can lead to infertility, metabolic disorders and other health problems. Ultrasound is an important tool for the diagnosis of PCOS. In medical image processing, the threshold segmentation plays a critical role in the identification of PCOS by isolating and accurately analyzing the cysts. In this paper, the PCOMorphology is extracted from the ultrasound images using three threshold-based segmentation algorithms: Adaptive Mean Threshold, Adaptive Gaussian Threshold, and Otsu Threshold. The performance of these algorithms is evaluated based on several metrics such as accuracy, sensitivity, specificity, and F1 score. The results show that the Otsu Threshold algorithm provides the best performance in terms of follicle detection and accuracy, followed by Adaptive Gaussian Threshold and Adaptive Mean Threshold. In conclusion, the comparison of these three algorithms shows that Otsu Threshold algorithm is the most suitable for the identification of PCOS using ultrasound images. The results of this study have important implications for the diagnosis of PCOS and further research is needed to improve the accuracy of the diagnosis. The proposed algorithms can be integrated into the existing clinical diagnosis systems to improve the accuracy and efficiency of the diagnosis process. This study provides a promising direction for future research in the field of medical image processing and PCOS diagnosis © 2023 IEEE.

关键词： image segmentation

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