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

共 13657 条记录，以下是751-760 订阅

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NeuroAI: Emerging Artificial Intelligence and image processing Techniques in Neuroscience for Enhanced Medical Diagnosis of Brain Tumor 1

NeuroAI: Emerging Artificial Intelligence and Image Processi...

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1st International conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Bhatt, Stuti Sharma, Sachin Bhadula, Shuchi Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350365016

The combination of Artificial Intelligence (AI) and Neuroscience speaks to an advantageous relationship with significant suggestions. AI, a multidisciplinary field in CS which points to imitate insights in machines, whereas Neuroscience explores the brain's structure and cognitive capacities. The union of these disciplines has driven to shared headways. Neuroscience has altogether affected AI, motivating profound neural systems based on natural neural systems. AI exceeds expectations in assignments such as NLP, object detection, and speech recognition. AI, drawing from neuroscience, utilizes support learning standards watched in people and creatures to create autonomous algorithms. AI's capacity to analyze complex information and reveal unapparent design is important for interpreting neuroscience information. AI interfaces can extricate neural signals from the human brain and interpret them into commands for gadgets like automated arms, supporting those with versatility impediments. AI moreover contributes to restorative areas, analyzing neuroimaging information and supporting early discovery of neurological disarranges. This paper conducts a comprehensive audit and usage of the AI and neuroscience cooperative energy, particularly brain tumor, emphasizing their merging for untimely revelation and forecast of neurological clutters. This paper primarily centers on early identifying and localizing the brain tumor locale utilizing division of patient's MRI pictures. © 2024 IEEE.

关键词： image enhancement

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image processing Based on Inter-frame Color Feature Matching for Visible Light Positioning

Image Processing Based on Inter-frame Color Feature Matching...

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Signal processing and Intelligent Computing (SPIC), International conference on

作者： Baozhu Yu Xiangyu Liu Yaoheng Chen Lei Guo Jianbo Bao Jiandong Sun School of Information Science and Engineering Shenyang Ligong University Shenyang China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China State Grid Electric Power Research Institute Nari Group Corporation Nanjing China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The indoor imaging visible light positioning (VLP) technology based on the image sensor (IS) utilizes existing indoor lighting infrastructure to provide high-precision indoor positioning services. However, due to the high complexity of traditional image processing algorithms, existing VLP systems are unable to provide real-time positioning services. In response to the above issue, we propose an image processing mechanism based on the inter-frame color feature matching method, which includes the equidistant sampling processing, low complexity decoding algorithm, and color feature matching-based LED region of interest (ROI) determination method. The results show that the system average positioning delay is 80.2ms, indicating better system real-time performance.

关键词： image sensors image color analysis Education Signal processing algorithms Lighting Signal processing Light emitting diodes Real-time systems Decoding Delays

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A Quality Evaluation Method for images in Adverse Weather Conditions

A Quality Evaluation Method for Images in Adverse Weather Co...

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Sensors, Electronics and Computer Engineering (ICSECE), IEEE International conference on

作者： Zhengxi Shao Jifeng Chen Wenxin Lin School of Information and Electromechanical Engineering Hunan International Economics University Changsha China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

By adopting an adaptive image quality evaluation method under adverse weather conditions, an advanced image defogging system has been developed. This system aims to predict the optimal algorithm for achieving the best image defogging results. The system integrates nine efficient defogging algorithms. To comprehensively evaluate the defogging performance of these algorithms, a set of evaluation metrics including PSNR, SSIM, LPIPS, ENL, MSE, and RMSE is employed to establish a comprehensive evaluation method. By calculating the values of these evaluation metrics, the optimal algorithm suitable for the image is predicted, and the defogged image produced by the optimal algorithm is processed to output the best-defogged image finally. All predicted data is stored in a database. When defogging an image, if there is matching data in the database, the corresponding optimal algorithm and image processing are directly selected, thereby enhancing the overall defogging efficiency.

关键词： Measurement image sensors image quality Adaptive systems Databases image processing Prediction algorithms Sensor systems Intelligent sensors Meteorology

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Asymmetric kernels for dead pixel replacement and cluster masking 33

Asymmetric kernels for dead pixel replacement and cluster ma...

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conference on Infrared Imaging systems - Design, Analysis, Modeling, and Testing XXXIII Part of SPIE Defense and Commercial Sensing conference

作者： Viljoen, Johan W. Goss, Tristan M. Hensoldt South Africa Pty Ltd Optron Div Nellmapius Dr ZA-0157 Irene South Africa

ISBN: (纸本)9781510650893;9781510650886

MWIR FPAs often contain a non-negligible number of dead pixels, and even clusters of pixels. In addition to the undesirable cosmetic effect that these flaws have on the output image, it can also be detrimental to downstream image exploitation efforts, including target detection and tracking algorithms. It is therefore necessary to mask such defects as early as possible, before it is exacerbated by the imaging pipeline processes, especially sharpening filters and contrast stretching, which are commonly used. This paper presents the results of an investigation into several dead-pixel replacement schemes of varying complexity, starting with simple replacement by the previous neighbor to interpolation using kernels of varying shapes and domain sizes. These are evaluated for accuracy, but also for their cluster-handling performance, latency impact, and suitability for real-time implementation on resource-constrained FPGA matrices. It is shown that asymmetric predictive kernels, optimized using neural nets or genetic algorithms, can offer significant improvements over naive last-good-neighbor replacement, while affording improved cluster-handling capability, low FPGA resource requirements, and incurring minimal and even zero latency.

关键词： dead pixel masking NUC image processing kernel integer math

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processing pipeline for automated data mining of the single astronomical objects from blurred CCD frames 12

Processing pipeline for automated data mining of the single ...

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12th International Scientific and Practical conference "Information Control systems and Technologies", ICST 2024

作者： Khlamov, Sergii Savanevych, Vadym Vlasenko, Vladimir Hadzhyiev, Emil Bondar, Yehor Netrebin, Yuriy Kharkiv National University of Radio Electronics Nauki avenue 14 Kharkiv61166 Ukraine National Space Facilities Control and Test Center Moskovska street 8 Kyiv01010 Ukraine INTTVE Limited O'Connell Bridge House D'Olier Street 2 DublinD02RR99 Ireland

In this paper the authors presented a sophisticated data mining pipeline, which was designed for restoring the high-quality images from the blurred frames made by the Charge-Coupled Device (CCD) cameras. The developed data mining pipeline leverages the modern informational technologies for the horizontal and vertical scalability. The core methodology integrates the following mathematical methods and algorithms: an inverse median filtration method for the noise reduction and the Lucy-Richardson algorithm for deblurring. The inverse median filtration effectively reduces impulsive noise while preserving edges, and the Lucy-Richardson algorithm iteratively refines the image by correcting for blurring effects encoded in the point spread function (PSF). The proposed system's architecture of the processing pipeline for automated data mining of the single astronomical objects from blurred CCD frames utilizes the following modern technologies: Python programming language, Redis, FastAPI, React, Docker, and Caddy to ensure high performance, scalability, and ease of deployment. This integrated approach significantly enhances the accuracy of astronomical observations, facilitating more precise studies of celestial objects. The proposed pipeline addresses the unique challenges of astronomical image processing, offering a robust solution for automated data mining of single astronomical objects. Our work demonstrates the potential to advance astronomical research by improving image clarity and reliability, contributing to various fields within astronomy. The combination of effective noise reduction and deblurring techniques, along with a scalable and high-performance system architecture, provides a comprehensive solution to the challenges faced in processing astronomical images. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Noise abatement

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Methodology for the empirical representation of image processing functions in EO/IR sensor system models and simulations 19

Methodology for the empirical representation of image proces...

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conference on Electro-Optical and Infrared systems - Technology and Applications XIX

作者： Hickman, Duncan L. Tektonex Ltd Argyll Rd Kilcreggan Argyll & Bute England

ISBN: (纸本)9781510655461

image processing design plays an important role in the overall performance of modern EO/IR cameras. Incorporating these functions into models and simulators presents a major design challenge, particularly for those cases where complex processing functions are required, such as those found in autonomous ATD/R systems. An established technique involves the use of image-based simulations which process pre- recorded imagery using representative image processing algorithms. However, such an approach requires extensive run times and a large volume of image data, both of which can be prohibitive. An alternative approach is presented here whereby a limited number of images are processed and then used to generate statistically based performance transfer functions utilising an appropriate interpolation scheme. These transfer functions are then used to represent the output response of the processing chain when the received imagery is subjected to different levels of degradations such as distortion and blurring. Such transfer functions can then be stored in multidimensional look-up tables which can be rapidly accessed by a system-level Monte Carlo performance simulation. The ability to represent and extract the performance-related transfer functions is dependent upon the image quality metrics, and the accuracy of the corresponding parametric model requires careful consideration of the model validation. An example simulation is presented based on an autonomous ATD/R sensor system mounted on an airborne platform. The importance of validation is demonstrated, and the increased run-time benefits are described. The proposed parametric image modelling approach provides sensor system designers with increased confidence in their design and compliance, and this helps reduces the early design risk.

关键词： image modelling empirical analysis parametric models image-flow models Monte Carlo simulations model validation ATR

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12th International conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th International conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819788354

The proceedings contain 34 papers. The special focus in this conference is on Recent Trends in Computing. The topics include: Deep Learning-Based Prediction of Human Ageing Related Proteins;the Insight of Clustering algorithms for Emerging Networking Paradigms;Revolutionizing Cardiovascular Health: Exploring the Potential of AI-Driven Approaches in Diagnosing Arrhythmia Disorders;humor Detection in Telugu Social Media Text Using Cost-Sensitive Learning and Indian Language Embeddings;empirical Comparison of Sleep Disorder Prediction Using Machine Learning Techniques;securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks;machine Learning-Powered Intrusion Detection for Network Security;secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality;deep Neural Networks for Early Diabetes Mellitus Prediction;advancements in image Deblurring and Performance Metrics Using Deep Learning Technique;emotion-Levitating Music Recommendation System Through Real-Time Facial Recognition;smartVoice TrashMate: The Intelligent Voice-Controlled Trash Bin;an Innovative image Enhancement Algorithm for Color Filter Array images: A Novel Approach;Enhancing Secure and Efficient Communication in Free-Space Optical Networks Using Spectral Amplitude Coding - OCDMA Technology;enhancing Citrus Fruit Classification and Quality Assessment Through Advanced Machine Learning Techniques;connecting Health for a Better Tomorrow Through Internet of Medical Things;image processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency;Leaf Checker: AI-Based Plant Diagnostic Tool Using Alexnet and Mobilenet;ioT-Supported Indoor Navigation System Using Machine Learning Technique;a Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities;An Unequal Cluster-Based Routing Mechanism Using Lyrebird Optimization Algorithm to Achieve Network Longevity in WSNs.

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Medical image Noise Reduction Methods Based on Cutting-Edge Hybrid Filtering Designs

Medical Image Noise Reduction Methods Based on Cutting-Edge ...

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International conference on Smart systems and Wireless Communication, SSWC 2024

作者： Saha, Tanusree Vishal, Kumar Magadh University Bodh Gaya India Department of Information Technology JIS College of Engineering Kalyani India Department of Mathematics Magadh University Bodh Gaya India

ISBN: (纸本)9789819613472

A high-resolution noise free medical image is an essential requirement for accurate disease detection. Medical image processing and analysis techniques have become utmost important in recent decades as a supplementary competence for medical professionals in the fields of diagnosis and prevention. Obtaining high-quality images without any interference is a crucial requirement for effectively managing therapeutic images. Medical images may contain many types of noises for involvement of simultaneous operation of multiple equipment involved in data capturing and transmission. Various filters have been suggested for eliminating different types of noises, each having its own merits and drawbacks depending on its capabilities and projected benefits. Our research depicts a technique for removing noises from medical images using a hybrid filtering approach based on ensemble adaptive mean filtering and Block-matching-3D filtering. The proposed methodologies specifically aim to reduce the speckle noise in brain MRI images obtained from ultrasonic therapy by employing hybrid filtering techniques. This proposed hybrid filtering algorithms integrate the most effective aspects of high-performance filters to achieve a de-noised image that retains fine details, edges, and textures. The statistical performance measurement metrics like Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) are employed to evaluate the improved visual quality of the images. The results indicate that our proposed hybrid filtering technique functioning efficiently and provide better quality de-noised image compare with other existing de-noising techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image enhancement

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12th International conference on Recent Trends in Computing, ICRTC 2024

12th International Conference on Recent Trends in Computing,...

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12th International conference on Recent Trends in Computing, ICRTC 2024

ISBN: (纸本)9789819789450

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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...

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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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