The paper presents the content based retinal image retrieval (CBRIR) based on dual tree complex wavelet transform (DT-CWT) and multi wavelet. The multi wavelet transform is more retrieval accuracy compare to DT-CWT. It is less complexity and less retrieval time. In this work, the proposed method follows two steps. The firstly, apply the wavelet transform (either DT-CWT or multi wavelet) and ene...

Based on wavelet packet decomposition and conditions of the support vector kernel function, a nonlinear wavelet basis is introduced to construct the kernel function of support vector machine (SVM). A tighten wavelet support vector machine (WSVM), which has strong generalization ability is also obtained. In this study, a novel damage classification method based on wavelet support vector machine ...

A binary multiresolution wavelet based framework for face identification is presented in this paper. This paper proposes the feature extraction algorithm based on multiresolution information of face. Proposed feature extraction algorithm extract discrete wavelet transform based binary features to represent face images.DWT plays very important role in efficient feature extraction which results i...

Speaker independent speech recognition system has been a challenging field of research since speech is the most basic and natural means of communication. In this work, a speech recognition system is developed for recognizing isolated words in Malayalam. Here we have used two wavelet based techniques namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD) for extracting f...

In this study, we have presented a method for detecting four common arrhythmias by using wavelet analysis along with the neural network algorithms. The method firstly includes the extraction of feature vectors with wavelet analysis. Then, the vectors will be categorized by means of the neural network into four classes. Input signals are recorded from two different leads. In addition, we have us...

In this work we compare two spatial and two wavelet-domain feature extraction methods that have been proposed in the recent literature for color-texture classification. The corresponding color-texture features, namely the Opponent-Color Local Binary Pattern distributions, the Chromaticity Moments, the Wavelet Correlation Signatures and the Color Wavelet Covariance features, are extracted in RGB...

In this study, we present an evaluation of using various methods for face recognition. As feature extracting techniques we benefit from wavelet decomposition and Eigenfaces method which is based on Principal Component Analysis (PCA). After generating feature vectors, distance classifier and Support Vector Machines (SVMs) are used for classification step. We examined the classification accuracy ...

In order to provide an accurate and rapid target recognition method for some military affairs, public security, finance and other departments, this paper studied firstly a variety of fuzzy signal, analyzed the uncertainties classification and their influence, eliminated fuzziness processing, presents some methods and algorithms for fuzzy signal processing, and compared with other methods on ima...

The purpose of feature extraction is to abstract substantial information from the original data input and filtering out redundant information. In this paper we transfer the hyperspectral data from the original-feature space to a scale-space plane by using a wavelet transform to extract significant spectral features. The wavelet transform can focus on localized signal structures with a zooming p...

This paper presents a comparison of wavelet and curvelet for lung cancer in term of diagnostic accuracy when each one is applied separately to the cluster K-Nearest neighbor classifier. Lung cancer is among the diseases that lead to high mortality rate globally. The computer aided diagnoisis system that is shown in this paper consists of a preprocessing state, a feature extraction stage (wavele...