Multiscale schemes for transferring information from fine to coarse scales are typically based on homogenization techniques. Such schemes smooth the fine scale features of the underlying fields, often resulting in the inability to accurately retain the fine scale correlations. In addition, higher-order statistical moments (beyond mean) of the relevant field variables are not necessarily preserv...

Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on dual-tree complex wavelet packet transform (DTCWPT) and multiple classifier fusion. In this method, in order to effectively extract underlying fault characteristic information, DTCWPT, which enjoys such attracti...

Multimedia databases deal with storage and retrieval of complex descriptors of image contents, called features. Traditional techniques consider features as “black boxes,” often represented as vectors for which the only operation defined is distance computation. This “modus describendi” resulted in the widespread utilization of similarity queries, which rest solely on the computation of distance...

This paper presents a new approach for the classification of the power systemdisturbances using support vector machines (SVMs). The proposed approach is carried out at three serial stages. Firstly, the features to be form the SVM classifier are obtained by using the wavelet transform and a few different feature extraction techniques. Secondly, the features exposing the best classification accur...

This work describes an ongoing project related to a new method to perform face recognition from video sequences. Faces are detected and tracked in a video sequence using Gabor wavelet networks. This process also allows locating and extracting facial feature regions around the eyes, nose and mouth. A modified Karhunen-Loève transform, defined with the aid of an automatic feature selection proced...

Background Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impact...

Algorithm encryption of wavelet image which has excellent localization property in time domain and frequency domain can locate graphic information having different directional characteristics into any precision level in certain extent. It has feature matched with human visual characteristics because of the disappearance of blocking effect and the noise. The article first analyzes the wavelet tr...

In this report, we propose a wavelet-based content descriptor with which we implement an image retrieval system. Initially, we propose the wavelet-based weighted standard deviation texture descriptor. We then show how to extend this descriptor to characterize both texture and color in images. Thus, we obtain a compact feature vector that characterizes images in terms of both texture and color. ...

In this work, we present a model theory based fusion methodology for multisensor wavelet-features based recognition called Automatic Multisensor Feature-based Recognition System (AMFRS). The goal of this system is to increase accuracy of the commonly used wavelet-based recognition techniques by incorporating symbolic knowledge (symbolic features) about the domain and by utilizing a model theory...

We show how the first order statistic, i.e. the histogram, of the wavelet filtered image is related to the higher order statistic of the original image. We then propose a texture classification method that uses the histogram information of the filtered images as the feature vector. Classification experiments show that the proposed method achieves better classification result than the Gabor wave...