Ridgelet - Open Access .click

Neurocomputing, 2007
A ridgelet kernel regression method is presented in this paper to approximate multi-dimensional functions, especially those with certain kinds of spatial inhomogeneities. This method is based on ridgelet theory, kernel and regularization techniques from which we can deduce a regularized kernel regression form.
Shuyuan Yang, Min Wang, Licheng Jiao
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

Ridgelet-based fake fingerprint detection

Neurocomputing, 2009
Perspiration phenomenon is very significant to detect liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore it may not be suitable for real-time authentications. Some other methods in the literature need extra hardware to detect liveness.
Suneeta Agarwal
exaly +2 more sources

A linear ridgelet network

Neurocomputing, 2009
The multiscale properties of the reception field of the human visual cortex have illuminated the research of wavelet neural network (WNN). Findings in neurophysiology indicate that in the human visual system there are specialized areas in the visual cortex that respond for particular orientations.
Shuyuan Yang 0001, Min Wang 0007, Licheng Jiao +2 more
openaire +1 more source

ridgelet transform
radon transform
wavelet transform

ridgelets
wavelets
wavelet

ridgelet neural network
finite ridgelet transform
watermarking