Blood pressure (BP) is a crucial indicator for assessing cardiovascular health, and effective monitoring contributes to early treatment of cardiovascular diseases. Photoplethysmography (PPG) has shown immense potentia...
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In the fields of computer vision and natural language processing. cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been mad...
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Transformer-based object tracking has become mainstream due to its high tracking performance at present. Most transformer-based trackers still classify the foreground and background and then calculate the bounding box...
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Automatic road graph extraction from aerial and satellite images is a long-standing challenge. Existing algorithms are either based on pixel-level segmentation followed by vectorization, or on iterative graph construc...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
Automatic road graph extraction from aerial and satellite images is a long-standing challenge. Existing algorithms are either based on pixel-level segmentation followed by vectorization, or on iterative graph construction using next move prediction. Both of these strategies suffer from severe drawbacks, in particular high computing resources and incomplete outputs. By contrast, we propose a method that directly infers the final road graph in a single pass. The key idea consists in combining a Fully Convolutional Network in charge of locating points of interest such as intersections, dead ends and turns, and a Graph Neural Network which predicts links between these points. Such a strategy is more efficient than iterative methods and allows us to streamline the training process by removing the need for generation of starting locations while keeping the training end-to-end. We evaluate our method against existing works on the popular RoadTracer dataset and achieve competitive results. We also benchmark the speed of our method and show that it outperforms existing approaches. Our method opens the possibility of in-flight processing.on embedded devices for applications such as real-time road network monitoring and alerts for disaster response.
It is currently unclear how to calculate the distance between two Pythagorean fuzzy sets. Several techniques have been proposed. However, not all available approaches can accurately exhibit differences between Pythago...
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Facial expression recognition from a single image has potential applications in fields including human-computer interaction and medical diagnosis. Most recent methods use deep neural networks to directly learn from a ...
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image segmentation is an important and difficult task in many medical applications. The segmentation results can be used to help doctors diagnose diseases, observe the lesion areas, and make surgical plans. Traditiona...
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Adversarial examples have raised public concern about the robustness of deep neural networks (DNNs). One universal approach to enhance the robustness is adversarial training which essentially augments the training dat...
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Implicit Neural Representations (INRs) are powerful to parameterize continous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolutio...
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Batteries which convert chemical energy into electrical energy and play an important role in our daily lives and across various industries. The chemical reaction that occurs within a battery during operation of an ele...
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