This paper implements a new leukemia identification method which depends on Mel frequency cepstral coefficient (MFCC) feature extraction and wavelet transform. Leukemia identification is a measurement of blood cell features for detecting the blood cancer of a patient. Blood cell feature extraction is based on transforming the blood cell two dimensional (2D) image into one dimensional (1D) signa...

The focus of this work is on the problem of feature extraction for vehicle detection. Feature extraction is a key point of pattern recognition. In particular, we propose using improved wavelet feature extraction approaches based on HSV space for rear-vehicle detection. Wavelet features are attractive for vehicle detection because they form a compact representation, encode edges, capture informa...

A wavelet feature selection derived by using fuzzy evaluation index for speaker identification is described. The concept of a flexible membership function incorporating weighed distance is introduced in the evaluation index to make the modeling of clusters more appropriate. Our results have shown that this feature selection introduced better performance than the wavelet features with respect to...

A new feature extraction method based on five moments applied to three wavelet transform sequences has been proposed and used in classification of prehensile surface EMG patterns. The new method has essentially extended the Englehart's discrete wavelet transform and wavelet packet transform by introducing more efficient feature reduction method that also offered better generalization. The appro...

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

The large numbers of images has posed increasing challenges to computer systems to store and manage data effectively and efficiently. This paper implements a CBIR system using different feature of images through four different methods, two were based on analysis of color feature and other two were based on analysis of combined color and texture feature using wavelet coefficients of an image. To...

To explore temporal dependencies and noise reduction in fMRI classification problem, we have tested two signal processing techniques, fourier tranform and wavelet transform, for feature creation. Gaussian Naive Bayes is applied on transformed data. We discussed comparison between fourier and wavelet transform, and perform various approaches, including feature selection, boosting and PCA, to exp...

We conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore introduce two feature sets: (1) the wavelet histogram signatures which capture all first order statistics using a model based approach and (2) the wavelet co-occurrence signatures, which reflect the coefficients' second-order statistics. The introduced feature sets outperform the t...

A newly developed distributed approximating functional ~DAF!-wavelet, the Dirichlet–Gabor DAF-wavelet ~DGDW!, is applied in a calculation of the state-to-state reaction probabilities for the three-dimensional ~3-D! (J50)H1H2 reaction, using the time-independent wave-packet reactant-product decoupling ~TIWRPD! method. The DGDWs are reconstructed from a rigorous mathematical sampling theorem, and...

In this paper, we present comparative analysis of scale-invariant feature extraction using different wavelet bases. The main advantage of the wavelet transform is the multi-resolution analysis. Furthermore, wavelets enable localization in both space and frequency domains and high-frequency salient feature detection. Wavelet transforms can use various basis functions. This research aims at compa...