This research studied the effect of variations in a sensor's F lambda/d metric value (FLD) on the performance of machine learning algorithms such as the YOLO (You Only Look Once) algorithm for object classificatio...
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This research studied the effect of variations in a sensor's F lambda/d metric value (FLD) on the performance of machine learning algorithms such as the YOLO (You Only Look Once) algorithm for object classification. The YOLO_v3 and YOLO_v10 algorithms were trained using static imagery provided in the commonly available training dataset provided by Teledyne FLIR systems. imageprocessing techniques were used to degrade image quality of the test dataset also provided by Teledyne FLIR systems, simulating detector-limited to optics-limited performance, which results in a variation of the FLD metric between 0.339 and 7.98. The degraded test set was used to evaluate the performance of YOLO_v3 and YOLO_v10 for object classification and relate the FLD metric to the probability of detection. Results of YOLO_v3 and YOLO_v10 are presented for the varying levels of image degradation. A summary of the results is discussed along with recommendations for evaluating an algorithm's performance using a sensor's FLD metric value. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
This research paper explores the application of singular value decomposition (SVD) in quantum imageprocessing (QIP), specifically focusing on the computation of eigenvalues using variational quantum algorithms. SVD i...
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The current research proposes a new zero watermarking approach that aims to protect medical images from tampering by utilizing the powerful learning capabilities of deep learning. In this paper, we propose a robust ze...
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The current research proposes a new zero watermarking approach that aims to protect medical images from tampering by utilizing the powerful learning capabilities of deep learning. In this paper, we propose a robust zero watermarking scheme based on multi-module fusion of VGG16, which enhances the efficiency and security of medical image transmission. The scheme leverages depthwise separable convolution to speed up feature extraction, enabling the focus on global image features. Furthermore, by incorporating the Convolutional Attention Module with channel and spatial attention, the extracted features are enhanced with stronger perceptual, discriminative, and generalization *** preserve multi-scale information and improve feature reusability, jump-joins are employed. The multi-module fusion of VGG16 facilitates the extraction of deep features with superior generalization, multi-scale, and high perceptual capabilities. Binary feature vectors are generated using a mean hash algorithm and encrypted using a novel dual chaos dual logistics encryption method designed in this paper, which significantly enhances both image watermark security and overall network transmission *** effectiveness of the scheme is verified through extensive experiments. Experimental results demonstrate that the proposed method achieves an NC value of over 0.92 in the presence of noise filtering and rotation attacks, indicating robust security and strong resistance to common image distortions. These results confirm the scheme's capability to provide high security for medical image transmission, ensuring both robustness against attacks and confidentiality in medical systems.
With the continuous development of digital imageprocessingalgorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion anal...
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Direction of Arrival (DOA) estimation plays a crucial role in array signal processing, with broad applications across various fields. Traditional Model-Based (MB) DOA estimation algorithms, however, are constrained by...
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image matching is a fundamental task in imageprocessing, particularly in photogrammetry and remote sensing. Conventional image matching methods often struggle with multi-modal images due to significant geometric dist...
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Recently, the collaborative operations of multi-domain unmanned vehicle systems have garnered significant attention, particularly regarding their visual perception capabilities under various complex weather conditions...
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ISBN:
(纸本)9789819607730;9789819607747
Recently, the collaborative operations of multi-domain unmanned vehicle systems have garnered significant attention, particularly regarding their visual perception capabilities under various complex weather conditions. Adverse weather such as rain, snow, and fog can severely degrade image quality, thereby affecting the efficiency and safety of unmanned vehicle systems to a certain extent. Therefore, image enhancement algorithms capable of handling multiple complex weather conditions are crucial for improving the visual perception capabilities of unmanned vehicle systems. This paper presents a unified image enhancement algorithm designed to address various complex weather conditions, such as rain, snow, and fog. The algorithm restores images by integrating spatial and frequency domain information. Experimental results fully demonstrate that this method can significantly enhance images under different adverse weather conditions and greatly improve image quality.
The world economy is threatened by counterfeit currencies. Counterfeit currencies are often difficult, time-consuming and ineffective to identify manually. Automated methods based on imageprocessing techniques and ma...
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Nanocomposites offer a unique material choice for enhancing sensor sensitivity and stability in electrochemical measurements. Nowadays, the feature extraction and imageprocessing of nanocomposites in electrochemical ...
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Artificial intelligence and intelligent algorithms have made remarkable progress in information processing. However, driven by the rapid development of the Internet, fake news interferes with people's access to ac...
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