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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 249 条记录，以下是21-30 订阅

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Comparative analysis of three advanced deep learning algorithms for Multiple Sclerosis lesion segmentation in FLAIR MRI

Comparative analysis of three advanced deep learning algorit...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Yi Zhu thomas Grenier Chantal Revol-Muller CREATIS University of Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

this paper addresses the pressing need for enhanced tools in the diagnosis and management of Multiple Sclerosis (MS), particularly in the accurate detection and segmentation of MS lesions. Leveraging recent advances in deep learning, we evaluate the performance of three state-of-the-art algorithms, focusing on their potential to improve both precision and efficiency in MS lesion segmentation from medical images. Our study provides critical insights into the strengths and limitations of each model, offering valuable guidance for future applications of AI in MS diagnosis and treatment.

关键词： Deep learning image segmentation Accuracy Multiple sclerosis Computational modeling Signal processing algorithms Signal processing Real-time systems Lesions Reliability

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Quantum Variational Singular Value Decomposition: A new hybrid approach towards quantum image compression

Quantum Variational Singular Value Decomposition: A new hybr...

引用

International Communication systems and Networks and Workshops, COMSNETS

作者： Britant Abhishek Tiwari CDAC Noida Uttar Pradesh India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

this research paper explores the application of singular value decomposition (SVD) in quantum image processing (QIP), specifically focusing on the computation of eigenvalues using variational quantum algorithms. SVD is a powerful mathematical tool in image processing, used for tasks such as image compression, noise reduction, and feature extraction. In this study, we propose quantum variational singular value decomposition (QVSVD) based on the variational quantum deflation (VQD) algorithm to determine the eigenvalues that contributes in calculating the singular values of the image matrix and eventually doing the SVD. this facilitates the extraction of eigenvalues with exponential speedup on real hardware compared to classical methods. We detail the implementation of this quantum algorithm within the framework of QIP, highlighting its advantages in terms of computational efficiency and accuracy on a Fault-Tolerant Quantum Computing (FTQC). Furthermore, we present a comparative analysis of the quantum and the classical method of SVD, demonstrating the accuracy of the image data. the simulation results validate the theoretical advantages of using quantum algorithms for SVD in image processing. this work not only underscores the potential of quantum computing in enhancing image processing techniques but also sets the stage for future research in the field, exploring more complex image processing tasks and other quantum algorithms. Our findings suggest that quantum image processing can offer unprecedented capabilities, paving the way for advancements in various applications such as medical imaging, remote sensing, and multimedia processing.

关键词： Training Quantum computing Quantum algorithm image coding Accuracy Feature extraction Eigenvalues and eigenfunctions Matrix decomposition Time complexity Singular value decomposition

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An Enhanced Bats Algorithm for Optimization thresholds in Medical image Segmentation 17th

An Enhanced Bats Algorithm for Optimization Thresholds in Me...

引用

17th International conference on Intelligent Information Hiding and Multimedia Signal processing (IIH-MSP) / 14th International conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Dao, thi-Kien Vu, Ngoc-thanh Nguyen, thi-Xuan-Huong Nguyen, Trong-the Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fujian Peoples R China Hai Phong Univ Med & Pharm Haiphong Vietnam Haiphong Univ Management & Technol Haiphong Vietnam VNU HCM Univ Informat Technol Multimedia Communicat Lab Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City 700000 Vietnam

ISBN: (纸本)9789811910531;9789811910524

the key to threshold segmentation is choosing the thresholds that will decide the segmentation's outcome-the calculation complexity increases as the number of thresholds increases, which causes challenges with traditional methods. this study introduces an enhanced bats algorithm (EBA) for selecting optimal picture segmentation thresholds and applies it to the global optimization issue of the segmentation function's objective function. the EBA blends well with picture segmentation that produces exceptional computation in global convergence and robustness and prevents trapping into local optimization. It is particularly well suited to solving complex functions with high dimensions and multiple peaks. Compared to GA and PSO, extensive theoretical study and simulation results reveal that EBA has greater efficacy, efficiency, stability of the range of thresholds, and quality in multi-image and multi-thresholds segmentation.

关键词： Enhanced bats algorithm Kapur entropy Optimization algorithms Picture threshold segmentation

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Enhancing Efficiency and Quality of image Caption Generation with CARU 17th

Enhancing Efficiency and Quality of Image Caption Generation...

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17th International conference on Wireless algorithms, systems, and Applications (WASA)

作者： Huang, Xuefei Ke, Wei Sheng, Hao Macao Polytechn Univ Fac Sci Appl Macau Peoples R China Macao Polytechn Univ Engn Res Ctr Appl Technol Machine Translat & Arti Minist Educ Macau Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Hangzhou Innovat Inst Yuhang Hangzhou 310023 Peoples R China

ISBN: (纸本)9783031192135;9783031192142

image caption is textual explanation automatically generated by a computer according to the content in an image. It involves both image and natural language processing, and thus becomes an important research topic in pattern recognition. Deep learning has been successful in accomplishing this task, and the quality of captions generated by existing methods is already high. However, due to the broadness and variety of image caption applications, the current generated captions are still not sufficiently detailed, and the training efficiency can also be improved. therefore, under the encoder-decoder framework of deep learning, how to use fewer parameters to improve the training efficiency and retain the quality of the generated image descriptions is a huge challenge. In this work, we introduce an improved method based on the encoder-decoder structure, adding an attention mechanism, and applying the content adaptive recurrent unit (CARU), as the decoder, to generate image captions. Inspired by GRU, CARU is designed to have comparable performance with fewer parameters, and is sensitive to the features in hidden layers. the experimental results show, based on MsCOCO dataset, the proposed method achieved better performance than that using GRU as the decoder, and took less training time, effectively improves the training efficiency.

关键词： image caption generation Content adaptive recurrent unit Feature extraction Deep learning Training efficiency

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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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the Self-calibration Parameter Generating Device of Workpiece processing Based on Machine Vision 17th

The Self-calibration Parameter Generating Device of Workpiec...

引用

17th Chinese Intelligent systems conference, CISC 2021

作者： Wang, Zhenqi Guo, Dong Qin, Weier Gong, Rui School of Electrical Engineering Liaoning University of Technology Jinzhou121001 China School of Machinery Branch Liaoning Vocational University of Technology Jinzhou121007 China

ISBN: (纸本)9789811663192

this design is based on the machine vision measurement technology to measure the precise size of the workpiece produced in the factory assembly line. the Raspberry Pi 4B is used as the controller, and the image is collected by the camera mounted at the bottom of the z-axis of the three-axis slide platform. the image information is reduced by image preprocessing algorithm to reduce the environmental noise of the image. Again by feature extraction and feature fitting method to image edge locating and fitting, and then through the size transform algorithm to get the size measurement, the measurement data with the workpiece design dimension data generated from the calibration parameters analysis table, through the Profibus bus transfer to processing equipment, complete processing equipment for processing the product feedback. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Segmentation and Classification of Dental Caries in Cone Beam Tomography images Using Machine Learning and image processing

Segmentation and Classification of Dental Caries in Cone Bea...

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17th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Kasputis Zanini, Luiz Guilherme Fischer Rubira-Bullen, Izabel Regina Dos Santos Nunes, Fatima de Lourdes Department of Computer Engineering and Digital Systems University of S˜ao Paulo S˜ao Paulo Brazil Department of Surgery Stomatology Pathology and Radiology University of S˜ao Paulo Bauru Brazil School of Arts Sciences and Humanities University of S˜ao Paulo S˜ao Paulo Brazil

Dental caries are caused by bacterial action that demineralizes tooth enamel and dentin. It is a serious threat to oral health and potentially leads to inflammation and tooth loss if not adequately treated. Cone Beam Computed Tomography (CBCT), a three-dimensional (3D) imaging technique used in dental diagnosis and surgical planning, can potentially contribute to detection of caries. this study aims at developing a computational method to segment and classify caries in CBCT images. the process involves data preparation, segmentation of caries regions, extraction of relevant features, feature selection, and training machine learning algorithms. We evaluated our method performance considering different stages of caries severity based on the International Caries Detection and Assessment System scale. the best results were achieved using a Gaussianfilter with a multimodal threshold with a convex hull for the region of interest segmentation, feature selection via Random Forest, and classification using a model based on k-nearest neighbors algorithm. We achieved outcomes with an accuracy of 86.20%, a F1-score of 86.18%, and a sensitivity of 83.35% in multiclass classification. these results show that our approach contributes to the early segmentation and classification of dental caries, thereby improving oral health outcomes and treatment planning. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Tooth enamel

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A Low-Complexity and High-Quality Reconstruction Algorithm for Terahertz Single-Pixel Imaging

A Low-Complexity and High-Quality Reconstruction Algorithm f...

引用

International conference on Signal processing and Communication systems, ICSPCS

作者： Chieh-Li Wang Yuan-Hao Huang Department of Electric Engineering Natioinal Tsing Hua University Hsinchu Taiwan R.O.C

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

Single-pixel imaging is a promising technique for Terahertz (thz) imaging, because it can largely reduce the numbers of expensive thz emitter and detector. Single-pixel imaging uses compressive sensing (CS) to measure the compressed thz signal and to recover the object image. this work presents a low-complexity and high-quality total variation augmented Lagrangian method (2D-TVALM) algorithm based on the well-known total variation augmented Lagrangian alternating-direction algorithm (TVAL3). TVAL3 is a convex optimization algorithm used to solve the problem of total variation (TV) regularization. By designing the sampling matrix and some mathematic approximation, this work largely reduces the computational complexity and speed up the convergence. the simulation results show that the reconstruction quality of the proposed algorithm is about 7 dB higher than TVAL3 in PSNR and the performance is much better than that of the other CS algorithms. In the computation time, the proposed algorithm is about 31 times faster than TVAL3. Finally, the average PSNR and elapsed time of the proposed algorithm are 38.43 dB and 0.0558 s respectively with 200 iterations in the 15 test patterns.

关键词： Terahertz wave imaging TV Simulation Signal processing algorithms Signal processing Approximation algorithms Sensors Computational complexity image reconstruction Terahertz communications

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A Methodology Based on Unsupervised Learning Techniques to Identify the Degree of Food processing 17

A Methodology Based on Unsupervised Learning Techniques to I...

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17th Iberian conference on Information systems and Technologies, CISTI 2022

作者： Marcos, Ignacio Fernández-Villacañas Bordel, Borja Cira, Calimanut-Ionut Alcarria, Ramón Universidad Politécnica de Madrid Madrid Spain

ISBN: (纸本)9789893334362

the level of consumption of processed foods is increasing globally, with a consequent impact on long-term human health due to the increasingly common diseases related to the metabolic syndrome. therefore, to prevent the continuous spread of these diseases, it is a need to accurately identify the degree of processing of different types of food. there are already several systems that provide rules to classify the level of processing. such as NOVA, but their limitations usually require that nutrition experts follow already established criteria to manually carry out the classification. In this work, we propose a methodology based on unsupervised learning to create food clusters that correspond to their level of processing. In this regard, we apply unsupervised machine learning algorithms to generate food clusters based on their nutritional properties. Training data were retrieved from the Food and Nutrient Database for Dietary Studies (FNDDS). the application of the proposed methodology allowed the differentiation of foods according to their level of processing with an acceptable level of performance. In the end, the proposed methodology is applied to another dataset containing nutrition information from a very large list of real recipes in order to estimate the rate of ultra-processed recipes. © 2022 IEEE Computer Society. All rights reserved.

关键词： Unsupervised learning

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Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis

Quantum-Enhanced Machine Learning for Precision Breast Cance...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Agniva Banerjee Dept. Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. this research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes.

关键词： image segmentation Accuracy Machine learning algorithms Software algorithms Artificial neural networks Machine learning Feature extraction Breast cancer Software Tumors

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