Results 121 to 130 of about 256 (167)
Some of the next articles are maybe not open access.
Related searches:
Related searches:
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, , Licheng Jiao
exaly +3 more sources
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, , Licheng Jiao
exaly +3 more sources
Texture classification using ridgelet transform
Pattern Recognition Letters, 2006Texture 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.
S Arivazhagan, L Ganesan
exaly +3 more sources
Ridgelet-based fake fingerprint detection
Neurocomputing, 2009Perspiration 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 +1 more
exaly +2 more sources
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 +2 more
openaire +1 more source
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 +2 more
openaire +1 more source
Image denoising with complex ridgelets
Pattern Recognition, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, Guangyi, Kégl, Balázs
openaire +2 more sources
The Ridgelet transform of distributions
Integral Transforms and Special Functions, 2014Jasson Vindas, Stevan Pilipovic
exaly +1 more source
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 +2 more
openaire +2 more sources
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 +2 more
openaire +2 more sources
Robust Digital Watermarking in the Ridgelet Domain
IEEE Signal Processing Letters, 2004In this letter, we propose a multiplicative watermarking method operating in the ridgelet domain. We employ the directional sensitivity and the anisotropy of the ridgelet transform (RT) in order to obtain a sparse image representation, where the most significant coefficients represent the most energetic direction of an image with straight edges ...
Patrizio Campisi +2 more
openaire +1 more source
BayesShrink Ridgelets for Image Denoising
2004The wavelet transform has been employed as an efficient method in image denoising via wavelet thresholding and shrinkage. The ridgelet transform was recently introduced as an alternative to the wavelet representation of two dimensional signals and image data.
Nezamoddin Nezamoddini-Kachouie +2 more
openaire +2 more sources

