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Full reference image quality assessment based on dual-space multi-feature fusion
At present, the majority of techniques for assessing image quality are limited to extracting features from an image in a single space. This paper...
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Large-Scale Swarm Control in Cluttered Environments
In the evolving era of social robots, managing a swarm of autonomous agents to perform particular tasks has become essential for numerous industries....
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MSTFDN: Multi-scale transformer fusion dehazing network
Most of the existing dehazing methods are based on learning and statistical priors. The convolutional neural network (CNN) is used in most...
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A 2D Cortical Flat Map Space for Computationally Efficient Mammalian Brain Simulation
We present a method for computationally efficient cortical brain simulation by constructing a 2D cortical flat map space on a regular grid....
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Spatial multi-scale attention U-improved network for blood vessel segmentation
Vessel segmentation in digital subtraction angiography (DSA) is of great significance for the diagnosis, evaluation and detection of cerebral...
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VSAN: A new visualization method for super-large-scale academic networks
As a carrier of knowledge, papers have been a popular choice since ancient times for documenting everything from major historical events to...
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Multi-scale-ResUNet: an improve u-net with multi-scale attention and hybrid dilation for medical image segmentation
The liver is one of the largest and most important organs in the human body. It maintains important life activities and is also one of the organs...
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FFR-SSD: feature fusion and reconstruction single shot detector for multi-scale object detection
Object detection is one of the most fundamental tasks toward image content understanding. Although numerous algorithms have been proposed,...
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MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection
Accurate detection of pedestrian lanes is a crucial criterion for vision-impaired people to navigate freely and safely. The current deep learning...
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Scale-adaptive gesture computing: detection, tracking and recognition in controlled complex environments
Complexity intensifies when gesticulations span various scales. Traditional scale-invariant object recognition methods often falter when confronted...
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A large-scale multi-objective evolutionary algorithm based on importance rankings and information feedback
For large-scale multi-objective optimization problems, the trade-off between convergence and diversity brings significant challenges for researchers....
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SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds
Registering urban point clouds is a pretty challenging task due to the large-scale, noise and data incompleteness of LiDAR scanning data. In this...
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A Dimension-Based Elite Learning Particle Swarm Optimizer for Large-Scale Optimization
The large-scale optimization problems (LSOPs) have been a hot research in evolutionary computation (EC) community. Although there have been many...
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Zoom-GAN: learn to colorize multi-scale targets
In recent years, the research of image colorization based on deep learning has made great progress. Most of the existing methods have achieved...
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YUVDR: A residual network for image deblurring in YUV color space
Motion blur removal caused by camera shake and object motion in 3D space has long been a challenge in computer vision. Although RGB images are...
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Large-scale knowledge graph representation learning
The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks....
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Randomized self-updating process for clustering large-scale data
This paper introduces the randomized self-updating process (rSUP) algorithm for clustering large-scale data. rSUP is an extension of the...
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Hypergraph p-Laplacians and Scale Spaces
The aim of this paper is to revisit the definition of differential operators on hypergraphs, which are a natural extension of graphs in systems based...
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Multi-scale variable precision covering rough sets and its applications
Granular computing is required for data mining and knowledge discovery because the complexity and imprecision of data in the real world are too...
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A multi-scale residual capsule network for hyperspectral image classification with small training samples
Convolutional Neural Network(CNN) has been widely employed in hyperspectral image(HSI) classification. However, CNN cannot attain the relative...
