Results 141 to 150 of about 1,320 (180)

CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography. [PDF]

Quant Imaging Med Surg
Ma F, Liu X, Wang S, Li S, Dai C, Meng J.   +5 more
europepmc   +1 more source

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Quaternion Ridgelet Transform and Curvelet Transform

Advances in Applied Clifford Algebras, 2018
The relationships between the Fourier, Radon, wavelet, ridgelet, curvelet transforms for real-valued functions have been extensively studied and are well known. The paper under review extends some of these relationships to quaternion-valued functions. A quaternion \(a\) can be represented as \[ a=a_0+a_1 i+a_2 j+a_3 k, \] with \[ ij=k,\; jk=i,\; ki=j,\;
Jiman Zhao
exaly   +2 more sources

Texture classification using ridgelet transform

Pattern Recognition Letters, 2006
Texture classification has long been an important research topic in image processing. Now a day's classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities.
Arivazhagan Selvaraj, L. Ganesan, T. G. Subash Kumar   +2 more
exaly   +2 more sources

Generalized Discrete Radon Transforms and Their Use in the Ridgelet Transform

Journal of Mathematical Imaging and Vision, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Flavia Colonna, Glenn Easley
exaly   +3 more sources

Ridgelet transform on Gelfand–Shilov‐type spaces

Mathematical Methods in the Applied Sciences
We construct Gelfand–Shilov spaces of type and discuss the continuity and boundedness of the ridgelet transform. We then extend the analysis to generalized functions and explore its application to the space of tempered ultradistributions. Finally, we extend our findings to Gevrey functions of compact support.
Ashwani Rao, Ashish Pathak
exaly   +3 more sources

M-band ridgelet transform based texture classification

Pattern Recognition Letters, 2010
The ridgelets overcome the shortcomings of wavelets and show great potential in texture classification. However, the ordinary rideglet transform inherits the weakness of the 2-band wavelet transform. That is, in the Radon domain, the wavelet transform decomposes a signal into channels that have the same bandwidth on a logarithmic scale.
Yu-Long Qiao, Chun-Yan Song, Chun-Hui Zhao   +2 more
exaly   +2 more sources

The Ridgelet transform of distributions

Integral Transforms and Special Functions, 2014
Stevan Pilipovic, Jasson Vindas
exaly   +1 more source

Generalized Finite Continuous Ridgelet Transform

Trends in Mathematics, 2022
Nitu Gupta, V R Lakshmi Gorty
exaly   +2 more sources

3D fast ridgelet transform

Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2004
In this paper, we present a fast implementation of the 3D ridgelet transform based on discrete analytical 3D lines: the 3D discrete analytical ridgelet transform (DART). This transform uses the Fourier strategy (the projection-slice formula) for the computation of the associated discrete Radon transform.
Carré, Philippe, Helbert, David, Andrès, Éric   +2 more
openaire   +1 more source

Ridgelet transform for quarternion-valued functions

International Journal of Wavelets, Multiresolution and Information Processing, 2016
Using the convolution of quaternion-valued functions on [Formula: see text], we define the ridgelet transform on square integrable quaternion-valued functions on [Formula: see text]. We also prove the properties of the ridgelet transform such as linearity, continuity, Parseval’s identity and inversion formula.
Lakshmanan Akila, Rajakumar Roopkumar
openaire   +2 more sources