Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.

Fast and robust image identification

Abstract: This paper presents an image identifier robust to common modifications. A multi-resolution Trace transform is introduced that constructs a set of 1D representations of an image. A binary identifier is extracted from each representation using a Fourier transform. Experimental evaluation of the algorithm and three state-of-the-art methods was carried out on a set of over 60,000 unique images with results demonstrating that the method outperforms the prior art methods in terms of detection, robustness and speed and achieves detection rate over 99% at a false-positive rate below 1 per million, with search speed exceeding 10 million image pairs per second on a desktop PC.

Block-median pyramidal transform: analysis and denoising applications

Abstract: A nonlinear multiscale pyramidal transform based on nonoverlapping block decompositions using the median operation and a polynomial approximation is considered. It is shown that this structure can be useful for denoising of oneand two-dimensional (1-D and 2-D) signals. Various denoising techniques are analyzed, including methods based on spatially adaptive thresholding and partial cycle-spinning algorithms. An analytical method for deriving the distribution function of the transform coefficients is also presented. This, in turn, can be used for the selection of thresholds for denoising applications.

A Multiresolution Model of Auditory Excitation Pattern and Its Application to Objective Evaluation of Perceived Speech Quality

Abstract: This paper proposes a multiresolution model of auditory excitation pattern and applies it to the problem of objective evaluation of subjective wideband speech quality. The model uses wavelet packet transform for time-frequency decomposition of the input signal. The selection of the wavelet packet tree is based on an optimality criterion formulated to minimize a cost function based on the critical band structure. The models of the different auditory phenomena are reformulated for the multiresolution framework. This includes the proposition of duration dependent outer and middle ear weighting, multiresolution spectral spreading, and multiresolution temporal smearing. As an application, the excitation pattern is used to define an objective measure of auditory distortion of a distorted speech signal compared to the undistorted one. The performance of this objective measure is evaluated with a database of various kinds of NOISEX-92 degraded wideband speech signals in predicting the subjective mean opinion score (MOS) and is compared with the fast Fourier transform (FFT)-based ITU-T PESQ P.862.2 algorithm. The proposed measure is found to achieve comparable correlation between subjective MOS and objective MOS as PESQ P.862.2, with a trend suggesting better correlation for the nonstationary degradations compared to the stationary ones. Further refinement of the measure for distortion types other than additive noise is anticipated

Wavelet-based three-dimensional descriptors of aggregate particles

Abstract: Morphological characteristics of stone aggregates, including particle shape, angularity, and surface texture, have a significant impact on the performance of hot-mix asphalt materials. To accurately identify and quantify these critical aggregate characteristics, well-defined particle descriptors are essential. Moreover, because a large number of irregular particles must be assessed to adequately characterize an aggregate material, descriptors that can be quantified with automated machines are preferred. In processing true three-dimensional (3-D) data from a laser scanner, wavelet-based 3-D particle descriptors are proposed as a way to characterize individual stone particles. Aided by the multiresolution analysis feature of the wavelet transform, these descriptors provide a generalized, comprehensive, and objective way of describing aggregates. This approach was implemented in conjunction with an automated laser-profiling device built for rapidly characterizing the size and shape properties of aggregate samples. Tests with this equipment have produced data that show strong correlations between the wavelet-based particle descriptors and visual perceptions of the aggregate morphological properties. These results demonstrate that the wavelet-based approach is a promising method for quantifying these important aggregate properties.

Wavelet-based feature extraction for palmprint identification

Abstract: The wavelet theory has become hot in the last few years for its important relative characters, such as, subband coding, multiresolution analysis and filter banks. In this paper, we propose a novel method of feature extraction for palmprint identification based on wavelet transform, which is very efficient to handle the textural characteristics of palmprint images at low resolution. The matching results show that the proposed feature extraction method is efficient in terms of matching accuracy and computational speed.