Multi modal medical image fusion in, for instance, combining information from computed tomography (CT) scans, positron emission tomography (PET) and magnetic resonance imaging (MRI) into one single dataset, improves t...
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Despite the widespread implementation of SCADA systems in factories for centralized data management, their functionality is restricted to devices equipped with sensors. Manual readings are still prevalent for critical...
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Due to the improvement in the car manifacture, the rate of road traffic accidents is increasing. To solve these problems, there is loads of attention in research on the development of driver assistance systems, where ...
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ISBN:
(纸本)9783031538292;9783031538308
Due to the improvement in the car manifacture, the rate of road traffic accidents is increasing. To solve these problems, there is loads of attention in research on the development of driver assistance systems, where the main innovation is traffic sign recognition (TSR). In this article, a special convolutional neural network model with high accuracy compared to traditional models is used for TSR. The Uzbek Traffic Sign Dataset (UTSD) applied in the zone of Uzbekistan was created, consisting of 21.923 images belonging to 56 classes. We proposed a parallel computing method for real-time processing of video haze removal. Our utilization can process the 1920 x 1080 video series with 176 frames per second for the dark channel prior (DCP) algorithm. 8.94 times reduction of calculation time compared to the Central processing Unit (CPU) was achieved by performing the TSR process on the Graphics processing Unit (GPU). The algorithms used to detect traffic signs are improved YOLOv5. The results showed a 3.9% increase in accuracy.
With the rapid development of computer vision and imageprocessing technology, scene imageprocessing under particular weather conditions has become an important research direction, especially in foggy conditions of t...
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Targeting systems are subject to multiple sources of error when operating in complex environments. To reduce the effect of these errors, modern targeting systems generally include both imaging and RF sensors. Data pro...
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ISBN:
(数字)9781510667044
ISBN:
(纸本)9781510667037
Targeting systems are subject to multiple sources of error when operating in complex environments. To reduce the effect of these errors, modern targeting systems generally include both imaging and RF sensors. Data processing then provides target detection and classification information, and the detection streams are combined using a data fusion scheme to produce an optimal target location estimate with an associated latency. In this paper, the performance of a multi- sensor system in a maritime application is investigated using a mathematical simulator that has been developed to provide the system performance error analysis for different engagement scenarios and test conditions. This simulator is described together with the sources of targeting error such as image motion blur and radar glint. Additionally, the impact of flare and chaff countermeasures on the targeting performance is reviewed in terms of different types of target recognition and tracking algorithms.
Object recognition systems on images allow to automate of various routine processes. Vehicle detection systems are becoming widespread and are being incorporated into, "smart city"initiatives, and traffic co...
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Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and imageprocessing applications invol...
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ISBN:
(纸本)9781665459068
Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and imageprocessing applications involving multiple correlated input signals. In this paper, we propose algorithms for convolutional SSA (CSSA) based on the alternating direction method of multipliers. Specifically, we address the CSSA problem with different sparsity structures and the convolutional feature learning problem in multimodal data/signals based on the SSA model. We evaluate the proposed algorithms by applying them to multimodal and multifocus image fusion problems.
This paper introduces the completed project development of a cutting-edge Vision Semantics image Captioner., a comprehensive platform aimed at generating contextually rich descriptions for images. Focused on leveragin...
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This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced imageprocessing techniques and machine learning a...
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ISBN:
(纸本)9798350375480;9798350375497
This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced imageprocessing techniques and machine learning algorithms. With the growing prevalence of respiratory and cardiovascular conditions worldwide, timely and precise diagnosis is paramount for effective patient care. The study begins with a comprehensive review of existing methodologies and technologies employed in the identification of chest diseases from X-ray images. It critically evaluates the strengths and limitations of current approaches, highlighting the challenges faced in achieving high accuracy, speed, and scalability. To address these issues, the project aims to develop an AI-powered system for medical image analysis. In response to these challenges, our research proposes a novel approach that integrates Inception V3 model and imagenet. We leverage a large dataset of annotated chest X-rays to train a deep neural network capable of recognizing subtle patterns indicative of various diseases, including pneumonia, pneumothorax, lung and cardiac abnormalities. The model is optimized to provide not only accurate diagnoses but also to minimize false positives and negatives. In conclusion, this research contributes to the ongoing efforts in utilizing chest X-ray images for disease identification, presenting a robust and efficient methodology that could revolutionize the current diagnostic landscape. The findings hold promise for the development of automated systems capable of assisting healthcare professionals in the accurate and timely detection of chest diseases, ultimately contributing to enhanced patient care and management.
Breast cancer is a significant global health concern, with early detection being critical for successful treatment and improved patient outcomes. In recent years, machine learning-based imageprocessing techniques hav...
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ISBN:
(纸本)9798350313987
Breast cancer is a significant global health concern, with early detection being critical for successful treatment and improved patient outcomes. In recent years, machine learning-based imageprocessing techniques have emerged as powerful tools in the field of medical imaging, particularly in breast cancer detection and diagnosis. This research paper explores the application of machine learning algorithms to enhance the accuracy and efficiency of breast cancer detection using various medical imaging modalities, such as mammography, ultrasound, and magnetic resonance imaging (MRI). The study begins by reviewing the current state of breast cancer detection methodologies and highlighting their limitations. It then delves into the utilization of machine learning algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and deep learning models, for the automated analysis and interpretation of breast cancer images. Various preprocessing steps, feature extraction techniques, and data augmentation methods are discussed to optimize the performance of these algorithms. Furthermore, the paper examines the integration of machine learning models with radiomics, genomics, and clinical data to create comprehensive breast cancer diagnostic systems. These integrated systems aim to provide more accurate risk assessment, personalized treatment recommendations, and improved patient management. The results of several case studies and clinical trials are presented to demonstrate the effectiveness of machine learning-based imageprocessing techniques in breast cancer detection. These studies illustrate how these techniques can improve sensitivity, specificity, and overall diagnostic accuracy compared to traditional methods. This research underscores the promising role of machine learning-based imageprocessing techniques in advancing breast cancer detection. It highlights the potential for early diagnosis and improved patient care, paving the way for mor
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