Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

In this paper, a new fast method for detecting human faces in color images using wavelet transform is proposed. The face detection algorithm has three stages, where chrominance, shape and frequency information are used respectively. The algorithm starts at the lower resolution version of the image obtained from the wavelet transform, so that the amount of data to be processed is greatly reduced...

This course gives an introduction to the design of efficient datatypes for adaptive waveletbased applications. It presents some code fragments and benchmark technics useful to learn about the design of sparse data structures and adaptive algorithms. Material and practical examples are given, and they provide good introduction for anyone involved in the development of adaptive applications. An a...

This paper presents a novel algorithm for detecting user-selected objects in given test images based on a new adaptive lifting scheme transform. Given an object as a template, we first select a set of coefficients as object features in the wavelet transform domain and then build an adaptive transform based on the selected features. The goal of the new adaptive transform is to vanish the selecte...

Image compression relies on e cient representations of images, and within smooth image regions, the wavelet transform provides such a representation. However, near edges, wavelet coe cients decay slowly and are expensive to code. We focus on improving the transform by incorporating adaptivity. Construction of non-linear lter banks has been discussed [1, 2], but the question of how to utilize th...

introduction: one of the major problems in the development of nuclear medicine images is the presence of noise. the noise level in nuclear medicine images is usually reduced by the analysis of imaging data in a fourier transform environment. the main drawback of this environment belongs to low signal to noise ratio in high frequencies because removing noise frequencies may remove data and times...

This course gives an introduction to the design of efficient datatypes for adaptive wavelet-based applications. It presents some code fragments and benchmark technics useful to learn about the design of sparse data structures and adaptive algorithms. Material and practical examples are given, and they provide good introduction for anyone involved in the development of adaptive applications. An ...

This paper presents a comparative study of implementation of feature extraction and classification algorithms based on discrete wavelet decompositions and Adaptive Network Based Fuzzy Inference System (ANFIS) for digital modulation recognition. Here, in first stage, 20 different feature extraction methods are generated by separately using Daubechies, Biorthogonal, Coiflets, Symlets wavelet fami...

It has been well established that state-of-the-art wavelet image coders outperform block transform image coders in the rate-distortion (R-D) sense by a wide margin. Wavelet-based JPEG2000 is emerging as the new high-performance international standard for still image compression. An often asked question is: how much of the coding improvement is due to the transform and how much is due to the enc...