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

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Parameter Blending For Multi-Camera Harmonization For Automotive Surround View systems

Parameter Blending For Multi-Camera Harmonization For Automo...

引用

Acoustics, Speech, and Signal processing Workshops (ICASSPW), IEEE International conference on

作者： Yuzhuo Ren Yining Deng David Pajak Robin Jenkin Niranjan Avadhanam Varsha Hedau NVIDIA Santa Clara CA USA

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

In a surround view system, the image color and tone captured by multiple cameras can be different due to cameras applying auto white balance (AWB), global tone mapping (GTM) individually for each camera. The color and brightness along stitched seam location may look discontinuous among multiple cameras which impacts overall stitched image visual quality. To improve the color transition between adjacent cameras in stitching algorithm, we propose harmonization algorithm which applies before stitching to adjust multiple cameras’ color and tone so that stitched image has smoother color and tone transition between adjacent cameras. Our proposed harmonization algorithm consists of AWB harmonization and GTM harmonization leveraging image Signal Processor (ISP)’s AWB and GTM metadata statistics. Experiment result shows that our proposed algorithm outperforms global color transfer method in both visual quality and computational cost.

关键词： Visualization Runtime image color analysis Signal processing algorithms Metadata Signal processing Cameras

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Design of a people access control system using the ESP32 module and Internet of Things for a sanitory facility in a shopping mall

Design of a people access control system using the ESP32 mod...

引用

2022 IEEE Engineering International Research conference, EIRCON 2022

作者： Casas Sanchez, Haggi Loayza Apena, Jean Palomino, Joel Paiva-Peredo, Ernesto Universidad Tecnológica Del Perú Faculty of Systems and Electronic Engineering Lima Peru

ISBN: (数字)9781665450829

ISBN: (纸本)9781665450829

Access control and management of customer in shopping centers is an important task. In this context, some shopping centers implemented payment system to access sanitary facilities. The collected money helps to keep the installations clean. According to managers the monetary report of workers is unprecise. To contrast the inaccurate reports, this research describes the design of a system to manage the data of people that access the toilets using the Internet of Things. The ESP32CAM module was programmed to capture images of people walking inside and outside of the facility. With the use of image processing and decision algorithms, the number of people that access the facility can be managed remotely through a web server. For this purpose, the information is sent to a cloud server using the Thingspeak platform. The system is design to register data from a triple access system where people can pay directly;show a receipt of consumption in the stores;or workers can show their identifications with RFID. © 2022 IEEE.

关键词： image processing

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Application of image Recognition algorithms Based on Deep Learning in the Field of Communication Science

Application of Image Recognition Algorithms Based on Deep Le...

引用

Integrated Intelligence and Communication systems (ICIICS), International conference on

作者： Yunxia Su Shanghai Publishing and Printing College Shanghai China

With the popularization of the internet, people have paid more and more attention to image processing, and deep learning has become a research hotspot. Analyzing images based on deep neural networks is a very valuable and promising direction for development. This article first introduced text representation methods and two commonly used classifiers in machine learning, namely the manual annotation model method. Then, it elaborated on the effectiveness and current application status of a fusion of multi-layer feedforward technology and convolutional neural networks for edge detection, feature extraction, and other fields. Finally, a new concept was proposed, and deep learning was applied to explain the important significance of image preprocessing in digital music file classification. Afterwards, a communication field training model based on image recognition algorithms was simulated and tested for its functionality. The test results showed that the image recognition algorithm based on deep learning had good noise processing performance during the propagation process, with an image clarity rate of over 90%. Through deep learning algorithms, automatic recognition and classification of radio signals can be achieved, including determining signal type, modulation method, frequency range, and other information. This is of great significance for radio spectrum monitoring, spectrum management, and interference source localization. By using deep learning algorithms, classification models for different types of radio signals can be trained, making the recognition of radio spectrum more accurate and reliable.

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MRI image denoising using U-Net and image processing Techniques 5

MRI image denoising using U-Net and Image Processing Techniq...

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5th IEEE International conference on Advances in Science and Technology, ICAST 2022

作者： Mehta, Darshil Padalia, Dishant Vora, Kush Mehendale, Ninad K.J Somaiya College of Engineering Department of Computer Engineering Mumbai India K.J Somaiya College of Engineering Department of Electronics and Telecommunication Mumbai India K.J. Somaiya College of Engineering Department of Electronics Mumbai India

ISBN: (纸本)9781665492638

Brain tumors are one of the leading causes of death, and hence it is critical to diagnose them early. MRI is the most effective diagnostic tool for detecting a tumor. However, thermal noise, temperature fluctuations, and other artifacts can generate noisy MRI scans, leading to inaccurate diagnoses. Deep learning algorithms combined with image processing techniques have aided in a variety of medical imaging tasks, including enhancing MRI images. Our work proposes a U-Net architecture with two encoder-decoder pairs for denoising MRI scans which were finely tuned on a dataset generated by injecting synthetic Gaussian noise. The model improved the Peak Signal to Noise Ratio (PSNR) from 11.90 to 30.96. The presented work also provides empirical evidence that the proposed denoising strategy enhances the prediction accuracy of brain tumors by nearly 23%. The developed denoising technique using U-Net would benefit radiologists and computer-aided diagnostic systems (CAD) in precisely diagnosing the disease by generating cleaner and clearer MRI scans. © 2022 IEEE.

关键词： image enhancement

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Digital Twin-Driven Bearing-Fault Detection in Induction Motor and Drives using Graph Sampling and Aggregation Network

Digital Twin-Driven Bearing-Fault Detection in Induction Mot...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Haraprasad Badajena Suryanarayan Majhi Bivash Chakraborty Mamata Jenamani Aurobinda Routray Ronit Dutta Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Industrial and Systems Engineering Indian Institute of Technology Kharagpur India Dept. of Electrical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Bearing fault diagnosis is crucial for ensuring the reliability and safety of industrial systems, particularly in preventing operational failures and maintaining product quality. Traditional signal processing methods and deep learning algorithms, while useful, often overlook the complex structural relationships within sensor data, limiting their diagnostic effectiveness. To address this, we present a novel Digital Twin-Driven Fault Diagnosis Framework that integrates graph-based learning techniques with advanced signal analysis. Our approach employs XGBoost and GraphSAGE embeddings to capture both spatial and temporal correlations within the current signals. The raw sensor data is then processed using a sliding window technique and time-frequency domain features are extracted then transformed into a graph structure that represents the intricate relationship in the signal. GraphSAGE is then applied to these graph structures, generating embeddings that enhance fault detection accuracy. Additionally, XGBoost is utilized for classification, improving the overall robustness of the system. The proposed method, deployed on edge devices, delivers real-time diagnostics, providing a scalable and efficient solution for industrial applications. Experimental results using real-world datasets demonstrate that our method significantly outperforms state-of-the-art algorithms, improving detection accuracy and Area under the curve (AUC) scores up to 97% and 99%, respectively.

关键词： Fault diagnosis Time-frequency analysis Accuracy Correlation Fault detection image edge detection Signal processing algorithms Feature extraction Real-time systems Speech processing

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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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Various algorithms and Techniques for Traffic Density Estimation 1

Various Algorithms and Techniques for Traffic Density Estima...

引用

1st International conference on Computational Science and Technology, ICCST 2022

作者： Venkat, Sanjay Sarkar, Swagata Shreemadhi, B. Siri, Karanam Poorna Bavanika, M. Sri Sairam Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665476553

Traffic has been a major problem in recent times. Traffic management is a must for safer and faster transportation. Automatic smart signal controlling systems respond to day-to-day world traffic densities to provide precedence and reduction in cross-road delays for travelers. According to the traffic density, the time between signals is adjusted so that the waiting time in traffic reduces. The ML model is capable of detecting accidents and immediately sends notifications or alert messages to nearby hospitals. The model tracks the GPS location of the ambulance and ensures no red signal in its pathway. Zebra crossing signals are also included, the signal timings are switched based on the crossing crowd. Automatic Smart Signal Control is a complete Machine Learning based project with some applications of IoT and Video / image processing. Implementation of the model all over the major signals and junctions provides faster and safer transportation. © 2022 IEEE.

关键词： image processing

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Automated Immune Cell Classification Using image processing Techniques

Automated Immune Cell Classification Using Image Processing ...

引用

Mobile Computing and Sustainable Informatics (ICMCSI), International conference on

作者： B. Sathiyaprasad Arva Hari Vardhan Reddy Usama Abdurrahman A. Annie Micheal Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

~ Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. This study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. The methodology incorporated pre-processing steps, like normalization and noise reduction. Then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. These models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. The data obtained can be used in high-throughput immunotherapy and disease diagnostic screening; it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. The work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders.

关键词： Deep learning Accuracy Immunology Computational modeling Biological system modeling Real-time systems Vectors Classification algorithms Streams Immune system

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ACM 2022 - Proceedings of the algorithms, Computing and Mathematics conference 2022

ACM 2022 - Proceedings of the Algorithms, Computing and Math...

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2022 algorithms, Computing and Mathematics conference, ACM 2022

The proceedings contain 11 papers. The topics discussed include: multiple ensembling techniques for monitoring the physical activities and predicting the performance of the students;a comprehensive review of influence node identification in complex networks;improving lifestyle of visually impaired people using virtual reality;algorithm for optimization in medical image processing applied in heterogeneous architecture;application of real-time operating systems in the design of medical devices;smart farming using artificial intelligence;optimized cluster centroids for segmentation of skin cancer using triangular intuitionistic fuzzy sets;analog and digital RoF spatial Mux MIMO-LTE system based A2 (arithmetic Aquila) optimization model for 5G network;comparison of state - of - art deep learning algorithms for detecting cyberbullying in twitter;and stock market technical analysis using Japanese candlesticks and machine learning.

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Machine Learning Approach for Detection of Diabetic Retinopathy with Improved Pre-processing

Machine Learning Approach for Detection of Diabetic Retinopa...

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International conference on Computing, Communication, and Intelligent systems (ICCCIS)

作者： Sharma, Ayushi Shinde, Swapnil Shaikh, Imran Ismail Vyas, Madhav Rani, Soumya Ramrao Adik Inst Technol Dept Informat Technol Nerul India

ISBN: (纸本)9781728185293

Diabetic Retinopathy has been one of the common reason why diabetic patients lose sight. It is caused by the damaging of the blood vessel of the light sensitive tissue at the back of the eye or the retina. The traditional methods for the detection of the disease requires lots of time and efforts hence the proposed system aims to detect diabetic retinopathy using image processing and Machine Learning. The input provided to the proposed system are the retinal images from the standard databases DIARETDB0 and DIARETDB1. The system aims to automate the process of detection using a combination of basic image processing steps with more focus on pre-processing to obtain clear image for feature extraction, further Machine Learning algorithms are applied for classification. The statistical features such as area and perimeter are obtained from blood vessels and exudates after pre-processing stage, different pre-processing techniques such as grayscale conversion, binarization, canny edge detection and some morphological operations including image dilation and erosion are applied to obtain clear image. The Machine Learning algorithms that are used for result analysis are Weighted KNN, Cubic SVM and Simple Tree which provides an accuracy of 85.8%, 87.2% and 88.6% respectively. The sensitivity and specificity parameters are also obtained, Simple tree yields better results for all the three result parameters.

关键词： Blood Vessels Diabetic Retinopathy Exudates Machine Learning Weighted KNN

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