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

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Memetic Algorithm Small Survey For 2019 Published Papers

Memetic Algorithm Small Survey For 2019 Published Papers

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Harco Leslie Hendric Spits Warnars Laurens Spits Warnars Wiranto Herry Utomo Antoine Doucet Arief Ramadhan Teddy Siswanto Department of Computer Science Computer Science Bina Nusantara University Jakarta Indonesia Department of Information Systems School of Information Systems Bina Nusantara University Jakarta Indonesia Master of Science and Technology President University West Java Indonesia Laboratoire L3i Université de La Rochelle La Rochelle France School of Computing Telkom University Bandung Indonesia Department of Information Systems Trisakti University Jakarta Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

The Memetic Algorithm (MA), introduced by Pablo Moscato in 1989, integrates Evolutionary algorithms with local search methods, enhancing its effectiveness in solving complex optimization problems. This paper provides a comprehensive survey of MA research published in 2019, reviewing 75 selected papers from an initial pool of 112 identified through Google Scholar. The selected papers were categorized into five types: optimization problems (40 papers), image processing (10 papers), parallel processing (5 papers), gene/DNA datasets (4 papers), and other applications (16 papers). The survey highlights MA’s versatility and effectiveness across various domains, particularly its potential for solving complex optimization problems. Key findings include the adaptability of MA for diverse applications, its ongoing relevance in addressing challenging issues, and promising opportunities for combining MA with other algorithms to enhance performance. The paper also emphasizes the significance of MA in fields such as image processing, where it improves pattern recognition and image enhancement, and in bioinformatics, where it optimizes gene selection and genetic algorithms. Despite the extensive study of MA, there remains a significant research gap in non-English literature, particularly in Bahasa, limiting accessibility for Indonesian researchers. This survey aims to bridge this gap by providing valuable insights and encouraging further exploration and application of MA to solve increasingly complex problems. It offers a comprehensive overview that underscores the importance of MA and its potential for future research and innovation.

关键词： Surveys Memetics Technological innovation Search methods Evolutionary computation Parallel processing Robustness Internet Ensemble learning Optimization

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Optimising Chest X-Rays for image Analysis by Identifying and Removing Confounding Factors

Optimising Chest X-Rays for Image Analysis by Identifying ...

引用

International conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2022

作者： Aslani, Shahab Lilaonitkul, Watjana Gnanananthan, Vaishnavi Raj, Divya Rangelov, Bojidar Young, Alexandra L. Hu, Yipeng Taylor, Paul Alexander, Daniel C. Jacob, Joseph Centre for Medical Image Computing University College London London United Kingdom Department of Computer Science University College London London United Kingdom Department of Radiology Royal Free London NHS Foundation Trust London United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Respiratory Medicine University College London London United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King’s College London London United Kingdom Health Data Research UK London United Kingdom

ISBN: (纸本)9789811667749

During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. This variation is seen in the CXR projections used, image annotations added and in the inspiratory effort and degree of rotation of clinical images. The image analysis community has attempted to ease the burden on overstretched radiology departments during the pandemic by developing automated COVID-19 diagnostic algorithms, the input for which has been CXR imaging. Large publicly available CXR datasets have been leveraged to improve deep learning algorithms for COVID-19 diagnosis. Yet the variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. COVID-19 diagnosis may be inferred by an algorithm from non-anatomical features on an image such as image labels. These imaging shortcuts may be dataset-specific and limit the generalisability of AI systems. Understanding and correcting key potential biases in CXR images is therefore an essential first step prior to CXR image analysis. In this study, we propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases. We perform ablation studies to show the impact of each individual step. The results suggest that using our proposed pipeline could increase accuracy of the baseline COVID-19 detection algorithm by up to 13%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Improving Teledermatology Referral with Edge-AI: Mobile App to Foster Skin Lesion Imaging Standardization 14th

Improving Teledermatology Referral with Edge-AI: Mobile App ...

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14th International Joint conference on Biomedical Engineering systems and Technologies

作者： Vasconcelos, Maria Joao M. Moreira, Dinis Alves, Pedro Graca, Ricardo Franco, Rafael Rosado, Luis Fraunhofer Portugal AICOS P-4200135 Porto Portugal Minist Hlth Shared Serv Lisbon Portugal

ISBN: (纸本)9783031206634;9783031206641

Every year, the number of skin cancer cases has been increasing which, consequently, increases the strain on the health care systems around the globe. With the growth of processing power and camera quality on smartphones, the investment in telemedicine and the development of mobile teledermatology applications can, not only contribute to the standardization of image acquisitions but also, facilitate early diagnosis. This paper presents a new process for real-time automated image acquisition of macroscopic skin images with the merging of an automated focus assessment feature-based machine learning algorithm with conventional computer vision techniques to segment dermatological images. Three datasets were used to develop and evaluate the proposed methodology. One comprised of 3428 images acquired with a mobile phone for this purpose and 1380 from the other two datasets which are publicly available. The best focus assessment model achieved an accuracy of 88.3% and an F1-Score of 86.8%. The segmentation algorithm obtained a Jaccard index of 85.81% for the SMARTSKINS dataset and 68.59% for the Dermofit dataset. The algorithms were deployed to a mobile application, available in Android and iOS, without causing any performance hindrances. The application was tested in a real environment, being used in a 10-month pilot study with six General and Family Medicine doctors and one Dermatologist. The easiness to acquire dermatological images, image quality, and standardization were referred to as the main advantages of the application.

关键词： Mobile dermatology image acquisition image quality assessment Feature extraction Machine learning image segmentation

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Mdbfusion: A Visible And Infrared image Fusion Framework Capable For Motion Deblurring

Mdbfusion: A Visible And Infrared Image Fusion Framework Cap...

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IEEE International conference on image processing

作者： Jun Chen Wei Yu Xin Tian Jun Huang Jiayi Ma School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Electronic Information School Wuhan University Wuhan China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

As a vital technology in the field of image processing, numerous visible and infrared fusion methods have emerged with the aim of generating a single image containing salient targets and abundant details. However, owing to the influence of target motion or camera shake, source images may suffer from motion blur, resulting in blurred edges and unrecognizable objects. To address this issue, this paper bridges the gap between deblurring and fusion tasks and proposes a joint motion deblurring and fusion network for visible and infrared image (MDbFusion). On the one hand, we innovatively merge motion deblurring task into network design, which effectively ensures the capacity of MDbFusion to process images in the extreme condition. On the other hand, an Adaptive Weight Module (AWM) is designed to calculate contribution between visible and infrared features, which solves the channel contrast between two tasks, greatly reducing the complexity of network. Extensive experiments demonstrate that the MDb-Fusion outperforms state-of-the-art (SOTA) fusion algorithms in terms of preserving texture details and quantitative metrics. Furthermore, we also compare with a two-step fusion strategy that first deblurring then fusion, both with SOTA methods. The results reveal superiority of our framework in coupling and reciprocity between two tasks.

关键词： Measurement Couplings Adaptive systems image edge detection Cameras Complexity theory Decoding

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Parallel algorithms Assessment Usage of image’s Segmentation Quality in Medicine 6

Parallel Algorithms Assessment Usage of Image’s Segmentatio...

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6th International conference on Computational Linguistics and Intelligent systems, COLINS 2022 - Volume I: Main

作者： Mochurad, Lesia Lema, Halyna Vilhutska, Roksolana Lviv Polytechnic National University 12 Bandera street Lviv79013 Ukraine

There is an increase in medicine data quantity and image resolution requirements due to the modern medicine development, which leads to the necessity of strong computing resources and huge computer memory amount during the appropriate tasks’ modeling. Semantic segmentation in medicine implements assessment functions in diagnostics. However, there is a human factor that can lead to a great number of mistakes, it is critically important to do everything to minimize and accelerate the diagnostic process which will positively affect the field that is connected to the life and health risks. A parallel algorithm is a way of minimizing these problems and boosting the total processing of medical data. That is why the topical subject of work efficiency determination of parallel algorithms methods of biomedical images’ segmentation is being investigated in this work. In particular, we investigated methods such as the Vinegar Method, Sobel’s filter, and Canny edge detector. Parallel computing technology CUDA has been chosen. An information system, which allows performing semantic image segmentation on the proposed parallel algorithm, was developed. The Sorensen index for semantic segmentation is about 0.45 and the acceleration of algorithms in some cases exceeds 10 times. Finished decision analysis, research of the subject area and scientific works related to this topic, as well as testing of the software product and identification of advantages and disadvantages in it are important except for this system projection. © 2022 Copyright for this paper by its authors.

关键词： Diagnosis

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HRRP Synthesis of Sparse Frequency Stepped Waveform with Time-Varying Amplitude Errors

HRRP Synthesis of Sparse Frequency Stepped Waveform with Tim...

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International conference on Vision, image and Signal processing (ICVISP)

作者： Shuang Cui Shuai Shao Hongwei Liu National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Compared to narrowband signals, the high-resolution range profile (HRRP) images of non-cooperative targets offer enhanced distance resolution and richer target information, which is crucial in the field of radar automatic target recognition. To alleviate the stringent hardware demands linked to wideband signal processing and minimize sampling point requirements, employing sparsely spaced frequency-hopping pulse trains presents an effective approach for HRRP generation. However, when the radar system is unstable, it can lead to parameter estimation errors. The amplitude of the radar echoes may vary over time, resulting in degraded image quality from traditional HRRP synthesis methods. To tackle this issue, this paper proposes an algorithm designed to mitigate the effects of time-varying amplitude (TVA) errors on synthesized HRRP, employing sparse frequency stepped waveform (SFSW). Extensive experimental results based on simulations demonstrate the effectiveness and robustness of this approach.

关键词： Frequency synthesizers Time-frequency analysis Target recognition Simulation Signal processing algorithms Radar Radar imaging Wideband Time-varying systems Signal resolution

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Development Approaches of Cloud-Based System for Object Recognition on images

Development Approaches of Cloud-Based System for Object Reco...

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International conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Yevhen Bershchanskyi Halyna Klym Department of Specialized Computer Systems Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

Object recognition systems have become integral in various domains, enhancing automation and decision-making processes. This research focuses on developing a cloud-based system specifically designed for efficient object recognition in images. Leveraging the scalability and flexibility of cloud computing, the proposed system not only performs accurate object detection but also ensures secure and scalable image storage. Implemented on the Amazon Web Services (AWS) platform, the system integrates advanced AI algorithms with cloud-native technologies to optimize resource usage and automate workload management. Key contributions include the design of a robust architecture that balances computational loads, enhances recognition accuracy, and streamlines image processing. This study demonstrates the potential of cloud technologies in simplifying object recognition tasks while maintaining security and efficiency.

关键词： Cloud computing image recognition Accuracy Text recognition Face recognition Roads Web servers Object recognition image storage Testing

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Transformer Fault Diagnosis Model Based on FI-CNN Method

Transformer Fault Diagnosis Model Based on FI-CNN Method

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International conference on Intelligent Traffic systems and Smart City (ITSSC)

作者： Lin, Nan Guo, Zhengwei State Grid Sichuan Elect Power Co Chengdu Peoples R China Sichuan Univ Coll Elect Engn Chengdu Peoples R China

ISBN: (数字)9781510652071

ISBN: (纸本)9781510652071;9781510652064

At present, the three-ratio method and artificial intelligence algorithm are widely used in transformer fault diagnosis. However, the three-ratio method based on DGA has problems such as lack of coding and insufficient anti-interference ability of various artificial intelligence methods, resulting in a high rate of misdiagnosis. This paper proposes a transformer fault diagnosis model based on FI (Feature image)-CNN. It takes the percentage of dissolved gas in the oil as an input parameter, constructs a feature vector, and extracts the two most widely distributed data as two-dimensional coordinates based on the characteristics of the fault. Axis, and then uses the RGB principle to construct a three-layer neuron structure with the remaining gas data to form a "RGB dynamic map" of the training sample;build a convolutional neural network model, combine the faulty DGA data, and use the DNN full-link network for fitting, and finally Realize the fault diagnosis of the power transformer. Finally, it is compared with the diagnostic performance of various optimization algorithms. The results show that the FI-CNN method can obtain high-dimensional curves that divide the fault categories according to the "RGB dynamic map" of the training samples and the corresponding spatial locations, and has higher diagnostic accuracy.

关键词： transformer fault diagnosis convolutional neural network deep learning image processing

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Utilising Machine Learning for Modulation and Signal processing

Utilising Machine Learning for Modulation and Signal Process...

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Communication & Computing (WCONF), World conference on

作者： Daljeet Pal Singh Madhav Singh Solanki Renuka Jyothi S Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Sanskriti University Mathura Uttar Pradesh India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively studied for modulation recognition, which is a hard hassle because of the always-changing and unpredictable nature of the sign. With the aid of leveraging ML strategies, which can be used to quantify and check specific houses of a signal, reliable modulation popularity and signal processing strategies can be evolved. For instance, ML can be used to automatically discover modulation class when the image shape of the signal is unknown. It is achieved via reading the sign with device learning techniques consisting of Fourier transform; wavelet-based totally function extraction, and convolutional neural networks. Different applications encompass channel estimation, signal detection, and optimization of verbal exchange networks. Further to recognizing modulation codecs, ML-based algorithms may be used to enhance signal processing and communique systems. It could be done with the aid of developing smarter systems that are capable of studying global data. ML can be used to robotically apprehend anomalous alerts and allow networks to quickly adapt to converting situations. ML techniques can also be used to optimize community parameters, which include power management, coding schemes, and modulation codecs, so as to maximize their performance.

关键词： Performance evaluation Wavelet transforms Codecs Shape Modulation Signal processing algorithms Feature extraction Classification algorithms Standards Signal detection

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Architectural Decoration image Recognition and Classification based on Convolutional Neural Network

Architectural Decoration Image Recognition and Classificatio...

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Intelligent systems and Computational Networks (ICISCN), International conference on

作者： Wenhao Zhang Jianyang Yang Mingjie Yang Weifang Engineering Vocational College Weifang China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Architectural decoration images are of various types and complex features. Therefore, fast and accurate recognition and classification of architectural decoration images has become a key challenge to improve design efficiency and project management level. This paper proposes an innovative solution - a CNN-based architectural decoration image recognition and classification method. The paper first collects and annotates various types of architectural decoration images to construct a dataset containing multiple architectural decoration styles and preprocesses these images. In the construction of CNN model, a deep network structure integrating convolutional layer, pooling layer, fully connected layer, etc. is designed. The CNN model is trained using the preprocessed architectural decoration image dataset, and the model parameters are adjusted through the back propagation algorithm to minimize the classification error. The results show that the average recognition accuracy of CNN in architectural decoration image recognition reaches 97.61%, which is significantly higher than the 91.35% of DeepLabv3+, and the processing speed is also faster. This paper provides a new technical means for the field of architectural decoration, which helps to promote technological progress and innovation in this field.

关键词： Backpropagation Training Technological innovation image recognition Accuracy Computational modeling Data models Classification algorithms Convolutional neural networks Unsupervised learning

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