Search results for "wavelet"

Multi-focus image fusion using local variability

2018

In this thesis, we are interested in the multi-focus image fusion method. This technique consists of fusing several captured images with different focal lengths of the same scene to obtain an image with better quality than the two source images. We propose an image fusion method based on Laplacian pyramid technique using Discrete Wavelet Transform (DWT) as a selection rule. We then develop two multi-focus image fusion methods based on the local variability of each pixel. It takes into account the information in the surrounding pixel area. The first method is to use local variability as an information in the Dempster-Shafer theory. The second method uses a metric based on local variability. …

Corrigendum to ‘Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor d…

2021

Regular theta-firing neurons in the nucleus incertus during sustained hippocampal activation

2015

This paper describes the existence of theta-coupled neuronal activity in the nucleus incertus (NI). Theta rhythm is relevant for cognitive processes such as spatial navigation and memory processing, and can be recorded in a number of structures related to the hippocampal activation including the NI. Strong evidence supports the role of this tegmental nucleus in neural circuits integrating behavioural activation with the hippocampal theta rhythm. Theta oscillations have been recorded in the local field potential of the NI, highly coupled to the hippocampal waves, although no rhythmical activity has been reported in neurons of this nucleus. The present work analyses the neuronal activity in t…

Discrete wavelet transform implementation in Fourier domain for multidimensional signal

2002

Wavelet transforms are often calculated by using the Mallat algorithm. In this algorithm, a signal is decomposed by a cascade of filtering and downsampling operations. Computing time can be important but the filtering operations can be speeded up by using fast Fourier transform (FFT)-based convolutions. Since it is necessary to work in the Fourier domain when large filters are used, we present some results of Fourier-based optimization of the sampling operations. Acceleration can be obtained by expressing the samplings in the Fourier domain. The general equations of the down- and upsampling of digital multidimensional signals are given. It is shown that for special cases such as the separab…

Data Compression with ENO Schemes: A Case Study

2001

Abstract We study the compresion properties of ENO-type nonlinear multiresolution transformations on digital images. Specific error control algorithms are used to ensure a prescribed accuracy. The numerical results reveal that these methods strongly outperform the more classical wavelet decompositions in the case of piecewise smooth geometric images.

Concatenated trial based Hilbert-Huang transformation on event-related potentials

2010

Time-frequency analysis is critical to study event-related potentials (ERPs) now. ERPs are usually generated through averaging over a number of trials, and such averaging limits the application of a nonlinear time-frequency analysis method—Hilbert-Huang transformation (HHT). This is because HHT usually requires very long recordings to sufficiently decompose the complicated signal into oscillations and the averaged ERP trace tends to possess only hundreds of samples. Thus, this study designs the concatenated trial based HHT to release the limitation on the decomposition. Such a paradigm may reveal better temporal and spectral properties of an ERP than the conventional wavelet transformation …

A taxonomy for wavelet neural networks applied to nonlinear modelling

2008

This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.

Edge detection insensitive to changes of illumination in the image

2010

In this paper we present new edge detection algorithms which are motivated by recent developments on edge-adapted reconstruction techniques [F. Arandiga, A. Cohen, R. Donat, N. Dyn, B. Matei, Approximation of piecewise smooth functions and images by edge-adapted (ENO-EA) nonlinear multiresolution techniques, Appl. Comput. Harmon. Anal. 24 (2) (2008) 225-250]. They are based on comparing local quantities rather than on filtering and thresholding. This comparison process is invariant under certain transformations that model light changes in the image, hence we obtain edge detection algorithms which are insensitive to changes in illumination.

Geometric Measurement Analysis Versus Fourier Series Analysis for Shape Characterization Using the Gastropod Shell (Trivia) as an Example

2003

Varied and efficient methods have been developed to describe and quantify natural objects. The most common ones use superimposition techniques (e.g. Procrustes methods; Bookstein, 1991), decomposition into harmonics (Fourier series and functions, wavelets; Anstey and Delmet, 1973; Christopher and Waters, 1974; Gevirtz, 1976; Lestrel, 1997; Toubin and others, 1999; Verrecchia, Van Grootel, and Guillemet, 1996; Younger and Ehrlich, 1977), analysis of spiral functions (e.g. Raup parameters; Raup, 1961, 1966; Tursch, 1998), and combinations of parameters from elementary geometry (e.g. circularity index, lengthening; Coster and Chermant, 1989; Schmidt-Kittler, 1986; Viriot, Chaline, and Schaaf, …

A pre-processing technique based on the wavelet transform for linear autoassociators with applications to face recognition

1997

In order to improve the performance of a linear autoassociator (which is a neural network model), we explore the use of several preprocessing techniques. The gist of our approach is to store, in addition to the original pattern, one or several pre-processed (i.e. filtered) versions of the patterns to be stored in a neural network. First, we compare the performance of several pre-processing techniques (a plain vanilla version of the autoassociator as a control, a Sobel operator, a Canny-Deriche operator, and a multiscale Canny-Deriche operator) on an example of a pattern completion task using a noise degraded version of a face stored in an autoassociator. We found that the multiscale Canny-D…