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The projectile’s penetration acceleration signal from the test is a kind of shock acceleration signal, and also is a kind of typical Nonstationary Random Signal(NRS),the duration of the signal is very short and its frequency components are rapidly changed with time. Empirical Mode Decomposition(EMD) is a novel method especially fit for dealing with the NRS ,it can decompose the signal into the limited...
For transformations, a set of new basis is normally chosen for the data. The selection of the new basis determines the properties that will be held by the transformed data. For wavelet transform, a set of wavelet basis aims to detect the localized features contained in microarray data. In this research, we investigate the performance of wavelet features based on wavelet detail coefficients at third...
3-D reconstruction from medical images is an important application of computer graphics and biomedicine image processing. Image segmentation is a crucial step in 3-D reconstruction. In this paper, an improved image segmentation method which is suitable for 3-D reconstruction is put forward. A 3-D reconstruction algorithm is used to reconstruct the 3-D model from images. First, rough edge is extracted...
In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on efficiency. Effective image features are selected by using properly chosen Gabor wavelets...
In this paper we propose a novel data driven strategy for designing Gabor wavelets for face recognition. Each face image is represented through a multi-sensor scheme, which splits the 2D frequency plane into a number of channels and identifies the most significant units for extracting information. The representative units for a set of face images are then derived based on statistical analysis of these...
This paper presents an application of multilevel wavelet analysis for high dimensional mass spectrometry data. Low frequency (approximation) coefficients, which contain major information contents of the mass spectra data, are extracted. Approximation of the spectra is reconstructed based on orthogonal wavelet approximation coefficients for locating the key m/z values of the mass spectra. Genetic algorithm...
In this paper we report our experience using different types of wavelets and different SVM kernel functions for classification of Magnetic Resonance Images to identify those showing symptoms of Alzheimer's Disease. We have developed a novel computational framework for extracting discriminative Gabor wavelet features from the images for classification using Support Vector Machines with various kernel...
A novel method for classification of magnetic resonance brain images is presented in this paper. We construct a computational framework for discriminative image feature subspaces. Magnetic resonance images of patients in Alzheimer's disease and normal brain MR images are classified with support vector machines. The framework for the novel method bases on the extraction of gabor features from 2D-magnetic...
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