The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer sc...
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We consider the problem of inferring high-dimensional data x in a model that consists of a prior p(x) and an auxiliary differentiable constraint c(x, y) on x given some additional information y. In this paper, the pri...
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ISBN:
(纸本)9781713871088
We consider the problem of inferring high-dimensional data x in a model that consists of a prior p(x) and an auxiliary differentiable constraint c(x, y) on x given some additional information y. In this paper, the prior is an independently trained denoising diffusion generative model. The auxiliary constraint is expected to have a differentiable form, but can come from diverse sources. The possibility of such inference turns diffusion models into plug-and-play modules, thereby allowing a range of potential applications in adapting models to new domains and tasks, such as conditional generation or image segmentation. The structure of diffusion models allows us to perform approximate inference by iterating differentiation through the fixed denoising network enriched with different amounts of noise at each step. Considering many noised versions of x in evaluation of its fitness is a novel search mechanism that may lead to new algorithms for solving combinatorial optimization problems. The code is available at https://***/AlexGraikos/diffusion_priors.
According to the characteristic that the position change of small infrared target in sequence images is more obvious than background and noise, an infrared small target detection algorithm based on multi-frame time do...
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ISBN:
(数字)9798331516550
ISBN:
(纸本)9798331516567
According to the characteristic that the position change of small infrared target in sequence images is more obvious than background and noise, an infrared small target detection algorithm based on multi-frame time domain information is proposed, called the Long-Short Term Frame Difference (LSTFD) algorithm. The algorithm first adopts the SURF (Speeded Up Robust Feature) alignment strategy to eliminate the relative motion of background pixels caused by camera shake and achieve global motion compensation. Then, the image sequence is constructed based on the current frame, and the computational results of long-interval difference and short-interval difference are combined to detect the infrared small targets. The experimental results show that the LSTFD algorithm can accurately detect moving targets with high detection accuracy and low false alarm rate under the condition of low signal-to-noise ratio. And the average detection rate can reach over 90% on five sets of experimental data.
In emergency rescue scenarios, rapid identification of human casualties is a critical first step in enhancing emergency medical response. This task can be limited by the physical and cognitive capacity of rescue perso...
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ISBN:
(纸本)9798350336702
In emergency rescue scenarios, rapid identification of human casualties is a critical first step in enhancing emergency medical response. This task can be limited by the physical and cognitive capacity of rescue personnel, who are exposed to significant risk. The use of small unmanned aerial systems (sUAS) equipped with autonomous casualty assessment abilities can reduce these limitations and risks by enabling remote casualty detection, identification, and vitals assessment, providing standoff protection, and eliminating the need for human personnel to access the potentially hazardous scene. This paper presents a vision-based casualty assessment framework and specifically discusses our casualty identification software, which is designed to recognize the faces of casualties and identify their nametapes in images captured by sUAS under realistic conditions. Our approach addresses the limitations of the sUAS-captured long-distance images to enable accurate identification in challenging casualty monitoring situations. The face and nametape recognition algorithms will be integrated into the larger casualty perception framework and embedded into sUAS platforms to assist with emergency rescue operations. The total casualty perception system will detect, identify, and evaluate the condition of casualties from a remote location, providing standoff protection to first responders and rapid information to inform a suitable medical treatment plan.
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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ISBN:
(数字)9798331531195
ISBN:
(纸本)9798331531201
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 is a powerful mathematical tool in imageprocessing, 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 imageprocessing. This work not only underscores the potential of quantum computing in enhancing imageprocessing techniques but also sets the stage for future research in the field, exploring more complex imageprocessing tasks and other quantum algorithms. Our findings suggest that quantum imageprocessing can offer unprecedented capabilities, paving the way for advancements in various applications such as medical imaging, remote sensing, and multimedia processing.
Despite the rapid advance of 3D-aware image synthesis, existing studies usually adopt a mixture of techniques and tricks, leaving it unclear how each part contributes to the final performance in terms of generality. F...
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Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification ...
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ISBN:
(数字)9798350372748
ISBN:
(纸本)9798350372755
Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification of potential targets. To improve the effectiveness of aircraft Search and Rescue missions, this study compares many object detection algorithms and imageprocessing methods. The investigated object detection methods are Mask R-CNN, SSD, and YOLOv8, and imageprocessing techniques are contrast improvement and image brightness control. The effectiveness of these strategies has been evaluated by using real aerial images that include different targets such as humans, vehicles, and fire. The results show how well the object detection algorithms work to accurately distinguish targets, and how an attractive imageprocessing approach improves the image performance of the best object detection model, as well as how the performance of the best object detection model with imageprocessing can improve the performance of model. This research shows that combining imageprocessing (contrast and brightness) with an optimal object detection algorithm (Mask R-CNN) significantly improves target detection in aerial search and rescue. While only Mask R-CNN performs best, adding imageprocessing increases its accuracy to 0.92, mAP to 0.89, and makes it more reliable in finding people in need. This shows the potential of this joint approach to save people in air rescue missions.
Automatic License Plate Recognition (ALPR) is an embedded real-time technology that automatically recognizes a vehicle's license plate. There are numerous uses, ranging from complex security to shared spaces, park...
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In order to cope with the problems of high target complexity, scale diversity, different resolutions and limited hardware resources in remote sensing image target detection, a lightweight and multi-modal remote sensin...
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ISBN:
(数字)9798350349115
ISBN:
(纸本)9798350349122
In order to cope with the problems of high target complexity, scale diversity, different resolutions and limited hardware resources in remote sensing image target detection, a lightweight and multi-modal remote sensing image target detection algorithm is proposed. The traditional convolutional layer (Conv) in the YOLOv8 network is replaced with GhostNet V2, which ensures the detection accuracy while achieving a lightweight network model; a Dual-Modal Fusion module (DMF) is built in the backbone network to integrate pixel-level RGB and IR modes to extract complementary information to enrich network feature information; the lightweight Efficient Channel Attention Mechanism (ECA) is introduced in the DFM module to ensure the lightweight of the model while alleviating channel information imbalance and improving the performance of target detection. The improved algorithm YOLOv8-LD achieved 89.5% mAP50, 16.3M Params, and 34.3G FLOPs on the DIOR data set. Experimental results on DOTA, NWPU and DIOR data sets show that compared with other algorithms, the proposed YOLOv8-LD algorithm achieves lightweight while improving detection accuracy.
Deep Learning comes under Machine Learning that accomplishes more power and flexibility by learning to present different concepts or relations of real world to simpler concepts. We use Deep learning fundaments in this...
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