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

共 12901 条记录，以下是51-60 订阅

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Identification of a Person Based on Block processing of a Signature image

Identification of a Person Based on Block Processing of a Si...

引用

2024 International Russian Smart Industry conference, SmartIndustryCon 2024

作者： Rizaev, Ildus Takhavova, Elza Shleimovich, Mikhail Kazan National Research Technical University Named after A.N. Tupolev-KAI Automated Systems for Information Processing and Control Department Kazan Russia

ISBN: (纸本)9798350395044

The development of computer vision systems stimulates the development of various applications in the field of image recognition. Methods and algorithms for image recognition in document processing systems play a crucial role. There is a lot of research in this direction, and one of the most difficult problems is handwritten text recognition. This includes the tasks of signature identification, which the authors of the paper consider. The article considers the main stages of signature analysis to establish authorship. They are the preprocessing of the signature, representation in a form convenient for applying author identification. A dataset containing sample signatures of various people is used. The described tools became means to develop software for preprocessing images of signatures, extracting characteristics and identifying the author of the signature. © 2024 IEEE.

关键词： Textures

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Beyond RGB: A Real World Dataset for Multispectral Imaging in Mobile Devices

Beyond RGB: A Real World Dataset for Multispectral Imaging i...

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IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Glatt, Ortal Ater, Yotam Kim, Woo-Shik Werman, Shira Berby, Oded Zini, Yael Zelinger, Shay Lee, Sangyoon Choi, Heejin Soloveichik, Evgeny Samsung Israel R&D Ctr Tel Aviv Israel Samsung Adv Inst Technol Suwon South Korea

ISBN: (纸本)9798350318920;9798350318937

Multispectral (MS) imaging systems have a wide range of applications for computer vision and computational photography tasks, but do not yet enjoy widespread adoption due to their prohibitive costs. Recently, advances in the design and fabrication of photonic metamaterials have enabled the development of MS sensors suitable for integration into consumer grade mobile devices. Augmenting existing RGB cameras and their processing algorithms with richer spectral information has the potential to be utilized in many steps of the image processing pipeline, but diverse real world datasets suitable for conducting such research are not freely available. We introduce Beyond RGB(1), a real-world dataset comprising thousands of multispectral and RGB images in diverse real world and lab conditions that is suitable for the development and evaluation of algorithms utilizing multispectral and RGB data. All the scenes in our dataset include a colorimetric reference and a measurement of the spectrum of the scene illuminant. Additionally, we demonstrate the practical use of our dataset through the introduction of a novel illuminant spectral estimation (ISE) algorithm. Our algorithm surpasses the current state-of-the-art (SoTA) by up to 58.6% on established benchmarks and sets a baseline performance on our own dataset.

关键词： algorithms algorithms Computational photography Datasets and evaluations image and video synthesis

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The Disease Prediction System based on Symptom Identification and image processing

The Disease Prediction System based on Symptom Identificatio...

引用

2024 International conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Swathi, M. Prithika, N.G. Sri Aishwariya, N. Jayaram, B. Dept.of.Computer Science And Business Systems Rmk Engineering College Thiruvallur India Dept.of.Computer Science And Business Systems Rmk Engineering College Thiruvallur India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366846

This paper aims to investigate a system that uses various machine-learning algorithms to predict symptoms and deep-learning techniques for image processing that leads to early disease prediction, an essential aspect of today's healthcare industry. The main objective of this system is to enable both the healthcare professionals and the patients to interpret the results at a faster rate. Existing systems provide results based on only symptoms which is not very effective in real-time but our system works well with both symptoms and image-based specifications. This investigation explores several algorithms like Random Forest, Decision tree, XGBoost, Logistic regression, Long-term short memory (LSTM), and Naïve Bayes for symptom-based prediction [14]. CNN (Convolutional Neural Network) deep learning techniques, notably DenseNet121, are used for image processing. The use of image processing bridges the gap between the user who feels symptom-based prediction to be a bit difficult. Furthermore, diverse experiments are done on multiple datasets to establish more accurate results. This system also ensures the safeguarding of sensitive information. The proposed disease prediction system helps empower healthcare professionals and individuals with proactive insights into potential health risks by giving accurate predictions for early intervention of the disease. © 2024 IEEE.

关键词： Forecasting

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Practical real-time image compression for resource-challenged devices

Practical real-time image compression for resource-challenge...

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conference on Multimodal image Exploitation and Learning

作者： Pham, Kevin Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673854;9781510673847

In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional image processing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption. This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution's performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware. As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved image processing in real-time applications while conserving computational resources and energy consumption.

关键词： image compression efficient AI real time processing

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An Adaptive and Interpretable Framework for Biomedical image Analysis 57

An Adaptive and Interpretable Framework for Biomedical Image...

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57th Asilomar conference on Signals, systems and Computers

作者： Singh, Samarth Acton, Scott T. Moosa, Shayan Sheybani, Natasha D. Univ Virginia Dept Biomed Engn Charlottesville VA 22903 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22903 USA Univ Virginia Hlth Syst Dept Neurosurg Charlottesville VA USA

ISBN: (纸本)9798350325744

Biomedical image analysis has benefited tremendously from the advent of artificial intelligence. Machine learning and deep learning-based algorithms are increasingly utilized in real time to assist clinicians with making crucial decisions for patients. Explainability and interpretability of these algorithms are critical for doctors and patients to develop trust in the automated decision making-process. Furthermore, designing specialized solutions based on clinical applications of these algorithms is non-trivial, due to the heterogeneity of imaging data available. Therefore, we propose an adaptive and interpretable framework for biomedical image analysis with novel applications to transcranial magnetic resonance-guided focused ultrasound thalamotomy for the treatment of essential tremor. The algorithm automatically configures itself to analyze brain lesions based on heterogeneous magnetic resonance images and subsequently predicts short-term clinical outcomes utilizing random forest and SHAP values, while ensuring interpretability for this process.

关键词： adaptive image processing interpretable AI XAI focused ultrasound medical imaging

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Effect of Deblurring on the Value of the Modulation Transfer Function of Electro-Optical Satellite images 32

Effect of Deblurring on the Value of the Modulation Transfer...

引用

32nd IEEE Signal processing and Communications Applications conference (SIU)

作者： Bayrakci, Edanur Yazici, Alper Berkin Erpolat, Talha Berk Unverdi, N. Ozlem Yildiz Tekn Univ Elekt & Haberlesme Muhendisligi Bolumu Istanbul Turkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978;9798350388961

Obtaining satellite images has created the need to process these images and turn them into meaningful data. Every day, new methods and algorithms are designed in the literature to meet this demand. These allow the improvement of parameters that evaluate the performance of imaging systems, such as the Modulation Transfer Function (MTF). Within the scope of this study, the MTF value was calculated by means of edge detection filters and mathematical operators. The algorithm was developed using the Python 3.11.1 programming language. The developed algorithm was tested for two different reference electro-optical satellite images. In this study, a literature search on the subject headings is included, and the methods and purposes of the procedures are explained.

关键词： Satellite image modulation transfer function image processing Fourier transform edge detection

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Revolutionizing Industries with Aerial Vehicle using Mobile for Surveillance 2

Revolutionizing Industries with Aerial Vehicle using Mobile ...

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2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Surendar, V Raghavendran, P. S. Poojitha, P. T. Shivali, S. Sakthivel, G. Lokesh, M. Kongu Engn Coll Dept EEE Perundurai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558;9798350379990

Revolutionizing industries with aerial vehicles using mobile for surveillance is the proposed title of this paper. The availability of drones has created several opportunities, particularly in the areas of agriculture, transportation, and security. This study is a follow-up to the creation of a drone controlled by Wi-Fi-based UAV and advanced image processing. To provide simple and seamless control, a mobile application has been released. Drone-taken photos and videos can also be processed and studied right away. Increasing the degree of autonomous vehicle operations, improving the accuracy of picture identification/analysis algorithms, and ensuring continuous communication between the drone and the control software are perhaps the key objectives of this work. The image processing component aims to take into account sophisticated improvements such as terrain analysis, object detection, and anomaly detection, which are crucial, particularly in the context of security surveillance. Ensuring steady and long-range connectivity while taking certain environmental elements into account can be critical. Enhancing the image processing technique to run smoothly on the off-board drone's hardware constraints is also a crucial step. However, integrating hardware and software continues to be a major challenge in the implementation of a dependable, high-accuracy, real-time analog-to-digital conversion system. The purpose of this research is to develop novel ways for improving UAV systems by integrating tough control systems with complicated image analysis capabilities. Keeping the aforementioned limits in mind, the purpose of this work is to improve understanding of how to create more self-sufficient and adaptive drones that can contribute to a broader range of professions or do more specific activities with minimal human interaction.

关键词： Wireless Fidelity Unmanned Aerial Vehicle App Cutting-edge Drone image processing

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imagery-Based Post-Flooding Infrastructure Damages Level Assessment

Imagery-Based Post-Flooding Infrastructure Damages Level Ass...

引用

conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace systems

作者： Guan, Shanyue Tao, Chengcheng Liu, Shufan Purdue Univ Sch Construct Management Technol W Lafayette IN 47907 USA

ISBN: (纸本)9781510660793;9781510660809

Coastal flooding events have caused many issues to infrastructure including bridges and highways. How to assess the flooding level and infrastructure damages in a low-cost, rapid, and accurate approach is critical to the infrastructure performance recovery. Due to the limited access to infrastructure during the post-flooding events, it is very challenging to evaluate infrastructure conditions closely. With the help of small unmanned aerial vehicles and onboard cameras, it provides the possibility to inspect the infrastructure conditions from images captured by drones remotely. With the additional help of image processing algorithms, it can help capture the infrastructure conditions and flooding levels from the imageries automatically with post-processing analysis. In this paper, we apply several different image processing algorithms to assess the infrastructure conditions by segmenting the flooding zone from the infrastructure. The performance of these algorithms in assessing infrastructure conditions is compared based on different factors with previously taken airborne imageries of infrastructure and flooding events. The performance of image processing is summarized and future work of assessing the infrastructure post-flooding damages is discussed.

关键词： Post-flooding infrastructure UAV image processing segmentation

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AI-SIPM 2024: International Workshop on Artificial Intelligence for Signal, image processing and Multimedia 24

AI-SIPM 2024: International Workshop on Artificial Intellige...

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4th Annual International conference on Multimedia Retrieval (ICMR)

作者： Ketcham, Mahasak Phodong, Kanyalag Pramkeaw, Patiyuth Yimyam, Worawut Chumuang, Narumol Songmuang, Pokpong Ganokratanaa, Thittaporn King Mongkuts Univ Technol Dept Informat Technol Management Bangkok Thailand Phranakhon Si Ayutthaya Rajabhat Univ Dept Comp Sci Phra Nakhon Si Ayutthaya Thailand King Mongkuts Univ Technol Thonburi Bangkok Dept Media Technol Program Bangkok Thailand Phetchaburi Rajabhat Univ Dept Business Informat Management Phetchaburi Thailand Muban Chombueng Rajabhat Univ Dept Digital Media Technol Bangkok Thailand Thammasat Univ Dept Comp Sci Bangkok Thailand King Mongkuts Univ Technol Thonburi Bangkok Dept Math Bangkok Thailand

ISBN: (纸本)9798400706028

The International Workshop on Artificial Intelligence for Signal, image processing, and Multimedia (AI-SIPM) aims to provide a platform for researchers, practitioners, and industry professionals to exchange ideas, discuss recent advancements, and explore future directions in the field of artificial intelligence (AI) applied to signal processing, image processing, and multimedia technologies. This workshop will feature presentations of novel research findings, practical applications, and innovative solutions addressing various challenges and opportunities in AI-driven signal and image processing, as well as multimedia analysis and understanding. Researchers and practitioners from academia, industry, and government agencies are invited to submit their original research contributions and participate in discussions that foster collaboration and knowledge sharing across different domains. Through this workshop, we aim to accelerate advancements in AI-driven technologies for signal processing, image analysis, and multimedia applications, contributing to the advancement of research and innovation in this rapidly evolving field.

关键词： algorithms signal processing image processing computer science artificial intelligence communication systems

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A Comprehensive Analysis of image processing Methods for Agricultural Product Quality Control

A Comprehensive Analysis of Image Processing Methods for Agr...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Nandhini, E. Vadivu, G. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

In the post-harvest stages of agricultural products, labor shortages and poor-quality control lead to significant market losses. The automated industries for agricultural products that use machine learning are evolving, with a recent focus on deep learning (DL). Computer vision (CV) serves as an essential part in the automation method for fruits and vegetables, where image processing (IP) ensures proper product categorization, addressing the issue of product loss. This paper focuses on IP techniques in sequential procedures, which can improve efficiency and precision in agricultural product sorting and grading. Deep neural networks and algorithms are effective because they achieve high classification accuracy with a low loss percentage. © 2024 IEEE.

关键词： Industries Accuracy Reviews image processing Scalability Quality control Loss measurement IP networks Reliability Sorting

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