Results 31 to 40 of about 4,542 (232)
Edge detection with trigonometric polynomial shearlets [PDF]
In this paper, we show that certain trigonometric polynomial shearlets which are special cases of directional de la Vallée Poussin-type wavelets are able to detect step discontinuities along boundary curves of periodic characteristic functions. Motivated
Kevin Schober+2 more
semanticscholar +1 more source
Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
Abstract Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge.
Mehdi Gheisari+10 more
wiley +1 more source
Different Faces of the Shearlet Group [PDF]
Recently, shearlet groups have received much attention in connection with shearlet transforms applied for orientation sensitive image analysis and restoration. The square integrable representations of the shearlet groups provide not only the basis for the shearlet transforms but also for a very natural definition of scales of smoothness spaces, called ...
Dahlke, Stephan+5 more
openaire +4 more sources
Gamma-convergence of a shearlet-based Ginzburg--Landau energy [PDF]
We introduce two shearlet-based Ginzburg--Landau energies, based on the continuous and the discrete shearlet transform. The energies result from replacing the elastic energy term of a classical Ginzburg--Landau energy by the weighted $L^2$-norm of a shearlet transform.
Philipp Petersen, Endre Süli
openalex +3 more sources
River boundary detection and autonomous cruise for unmanned surface vehicles
The detection of river boundaries is important for judging the drivable area of USVs, and it can also be utilized to ensure driving safety by limiting the effective drivable areas of the USVs in the river areas. This work proposes a real‐time detection method for river boundaries based on a LiDAR sensor to detect the boundaries of incompletely ...
Kai Zhang+5 more
wiley +1 more source
Inhomogeneous shearlet coorbit spaces [PDF]
In this paper, we establish inhomogeneous coorbit spaces related to the continuous shearlet transform and the weighted Lebesgue spaces [Formula: see text] for certain weights [Formula: see text]. We present an inhomogeneous shearlet frame for [Formula: see text] which gives rise to a reproducing kernel [Formula: see text] that is not contained in the ...
Fabian Feise, Lukas Sawatzki
openaire +3 more sources
Embeddings of anisotropic Besov spaces into Sobolev spaces
Abstract We study the embeddings of (homogeneous and inhomogeneous) anisotropic Besov spaces associated to an expansive matrix A into Sobolev spaces, with a focus on the influence of A on the embedding behavior. For a large range of parameters, we derive sharp characterizations of embeddings.
David Bartusel, Hartmut Führ
wiley +1 more source
Review of surface defect detection of steel products based on machine vision
Abstract Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state‐of‐art of vision‐based surface defect inspection technology of steel products by investigating about 170 publications.
Bo Tang+3 more
wiley +1 more source
Homogeneous approximation property for continuous shearlet transforms in higher dimensions
This paper is concerned with the generalization of the homogeneous approximation property (HAP) for a continuous shearlet transform to higher dimensions. First, we give a pointwise convergence result on the inverse shearlet transform in higher dimensions.
Yu Su, Wanchang Zhang, Wenting Su
doaj +1 more source
An unsupervised multi‐focus image fusion method based on Transformer and U‐Net
Abstract This work presents a multi‐focus image fusion method based on Transformer and U‐Net with an unsupervised training fashion. In this work, the authors introduce Transformer into image fusion because it has great ability to capture the global dependencies and low‐frequency features. In image processing, convolutional neural network (CNN) has good
Xin Jin+5 more
wiley +1 more source