Results 101 to 110 of about 157 (131)

Ridgelet Methods for Linear Transport Equations

2015
In this paper we present an overview of a novel method for the numerical solution of linear transport equations, which is based on ridgelets and has been introduced in [12, 16]. Such equations arise for instance in radiative transfer or in phase contrast imaging.
Grohs Philipp, Obermeier Axel
openaire   +2 more sources

Fingerprint classification by Block Ridgelet and SVM

10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010), 2010
The present article focuses on the classification of fingerprints. Our aim goal is to unify the process of fingerprint compression, classification and identification. The well known methods suited to these tasks are based on WSQ (Wavelet Scalar Quantization) for compression, Gabor filters for classification and minutiae matching for identification.
Amina Serir, Farida Bennabes
openaire   +1 more source

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.
Shankar Bhausaheb Nikam, Suneeta Agarwal
openaire   +1 more source

Incremental constructive ridgelet neural network

Neurocomputing, 2008
In this paper, a new kind of neural network is proposed by combining ridgelet with feedforward neural network (FNN). The network adopts ridgelet as the activation function in the hidden layer, and an incremental constructive method is employed to determine the structure of the network.
Shuyuan Yang 0001, Min Wang 0007, Licheng Jiao   +2 more
openaire   +1 more source

A New Adaptive Ridgelet Neural Network

2005
In this paper, a new kind of neural network is proposed by combining ridgelet with feed-forward neural network (FNN). The network adopts ridgelet as the activation function in hidden layer of a three-layer FNN. Ridgelet is a good basis for describing the directional information in high dimension and it proves to be optimal in representing the functions
Shuyuan Yang 0001, Min Wang 0007, Licheng Jiao   +2 more
openaire   +1 more source

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,\;
Ma, Guangsheng, Zhao, Jiman
openaire   +1 more source

Radon/ridgelet signature for image authentication

2004 International Conference on Image Processing, 2004. ICIP '04., 2005
In this paper, we describe a novel content-based image signature for authentication using the ridgelet transform. The signature is extracted from the Radon domain and entropy coded after a 1D wavelet transform, which is essentially the so-called "ridgelet transform". Unlike traditional authentication signatures, it has the ability to localise tampering
Zhen Yao, Nasir M. Rajpoot
openaire   +1 more source

Ridgelet moment invariants for pattern recognition

2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2012
Moment invariants have been a hot research topic for several decades already. Even though existing moment invariants are good for applications like pattern recognition, there is still a need to further improve the existing moment invariants published in the literature.
Guangyi Chen 0001, Scott Gleason 0001
openaire   +1 more source

EXTENDED RIDGELET TRANSFORM ON DISTRIBUTIONS AND BOEHMIANS

Asian-European Journal of Mathematics, 2011
The ridgelet transform is extended to the space of Schwartz distributions and to the space of C∞-Boehmians consistent with the classical Ridgelet transform on the space of square integrable Boehmians. The properties of the ridgelet transform like linearity, injectivity, surjectivity and continuity with respect to two notions of convergence are ...
openaire   +2 more sources

Image denoising based on ridgelet

6th International Conference on Signal Processing, 2002., 2002
Image denoising is an important step in the pre-processing of images; the noisy images bears different characteristics. For an anisotropic image, wavelets lose their effects on singularity detection because discontinuities across edges are spatially distributed.
null Hou Biao, null Jiao Li-cheng, null Liu Fang   +2 more
openaire   +1 more source