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Scale-preserving automatic concept extraction (SPACE)
Convolutional Neural Networks (CNN) have become a common choice for industrial quality control, as well as other critical applications in the...
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Guided Scale Space Radon Transform for linear structures detection
Using integral transforms to the end of lines detection in images with complex background makes the detection a hard task needing additional...
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Uncertainty Quantification for Scale-Space Blob Detection
We consider the problem of blob detection for uncertain images, such as images that have to be inferred from noisy measurements. Extending recent...
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Scale Space and Variational Methods in Computer Vision 9th International Conference, SSVM 2023, Santa Margherita di Pula, Italy, May 21–25, 2023, Proceedings
This book constitutes the proceedings of the 9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023, which...
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Multi-scale space-time transformer for driving behavior detection
The advent of advanced in-vehicle sensors and communication technologies have facilitated the collection of large volume and almost real-time data on...
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Scale space tracker with multiple features
Object tracking in videos has been a hot research for decades. Many approaches have been applied to improve the visual tracking, a challenging task...
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Generalised Scale-Space Properties for Probabilistic Diffusion Models
Probabilistic diffusion models enjoy increasing popularity in the deep learning community. They generate convincing samples from a learned...
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Learning metric space with distillation for large-scale multi-label text classification
Deep neural network-based methods have achieved outstanding results in the task of text classification. However, the relationship of text–label and...
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DeepGCSS: a robust and explainable contour classifier providing generalized curvature scale space features
In this paper, we build a novel, robust, and explainable deep neural network architecture for contour classification whose feature extraction layers...
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Multi-Space Evolutionary Search for Large-Scale Multi-Objective Optimization
Besides solving large-scale single objective optimization problems, this chapter further demonstrate the multi-space evolutionary search for...
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Multi-Space Evolutionary Search for Large-Scale Single-Objective Optimization
Today, because of the exponential growth of the volume of data in big data applications, large-scale optimization problems (i.e., optimization...
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Inverse Scale Space Iterations for Non-Convex Variational Problems: The Continuous and Discrete Case
Nonlinear filtering approaches allow to obtain decomposition of images with respect to a non-classical notion of scale, induced by the choice of a...
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Fine-grained scale space learning for single image super-resolution
Recent deep convolutional neural networks have achieved great reconstruction accuracy for single image super-resolution (SISR). Most of them,...
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Construction Technology of Multi-scale Perception Model of Urban Green Space
The “scale” of urban green space perception measurement is different from the connotation of “resolution” but refers to the perspective and dimension...
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Real-time tracking of moving objects through efficient scale space adaptation and normalized correlation filtering
The field of real-time mobile object tracking is a crucial aspect of computer vision. Despite numerous algorithms proposed for efficient tracking,...
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Multi-scale and Multi-stage Deraining Network with Fourier Space Loss
The goal of rain streak removal is to recover the rain-free background scenes of an image degraded by rain streaks. Most current deep convolutional...
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Silhouette Vectorization by Affine Scale-Space
Silhouettes are building elements of logos, graphic symbols and fonts. These shapes can be designed and exchanged in vector form, but more often they...
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Deep semantic space guided multi-scale neural style transfer
This paper mainly studies the Neural Style Transfer (NST) problem based on convolutional neural networks (CNN). Existing deep style migration...
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Analysis and classification of gait patterns in osteoarthritic and asymptomatic knees using phase space reconstruction, intrinsic time-scale decomposition and neural networks
Artificial intelligence (AI) has gained significant traction in medical applications. This study focuses on knee joint diseases, specifically...
