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.

A motion-compensated overcomplete temporal decomposition for multiple description scalable video coding

Abstract: We present a new multiple-description coding (MDC) method for scalable video, designed for transmission over error-prone networks We employ a redundant motion-compensated scheme derived from the Haar multiresolution analysis, in order to build temporally correlated descriptions in a t +2D video coder Our scheme presents a redundancy which decreases with the resolution level This is achieved by additionally subsampling some of the wavelet temporal subbands We present an equivalent four-band lifting implementation leading to simple central and side decoders as well as a packet-based reconstruction strategy in order to cope with random packet losses

Experimental Performances of the Single-Phase Wavelet-Modulated Inverter

Abstract: This paper presents the real-time implementation and experimental performances of the wavelet-modulation technique for single-phase voltage-source (VS) inverters. The wavelet-modulation technique is realized through constructing a nondyadic-type multiresolution analysis, which supports sampling of a sinusoidal reference-modulating signal in a nonuniform recurrent manner, then reconstructing it using the inverter-switching actions. The required nonuniform recurrent sampling is carried out by using dilated and translated sets of wavelet basis functions, which are generated by the scale-base linearly combined scaling function. The reconstruction of the sampled signal is accomplished by using dilated and translated sets of wavelet basis functions, which are generated by the scale-base linearly combined synthesis scaling function. The dilated and translated sets of wavelet basis functions used in the reconstruction are employed as switching signals to activate the inverter-switching elements. The wavelet-modulation technique is implemented in real time by using a digital signal processing board to generate switching pulses for a single-phase VS H-bridge (four-pulse) inverter. Experimental performances of the single-phase inverter, which is operated by the wavelet-modulation technique are investigated while supplying linear, dynamic, and nonlinear loads with different frequencies. Experimental test results show that high magnitude of fundamental components and significantly reduced harmonic contents of the inverter outputs can be achieved using the wavelet-modulation technique. The efficacy of the developed modulation technique is further demonstrated through performance comparisons with the pulsewidth- and random-pulsewidth-modulation techniques for similar loading conditions.

Real-Time Classification of Traffic Signs

Abstract: A challenging real-time imaging problem is classifying video traffic signs in background clutter under rotation, scale, and translation invariant conditions. Normalized Gabor Wavelet Transform features from multi-resolution filters were originally biologically-based; however, optimized features proved more effective. Two whole image template matching techniques were unsuccessful. A statistical pattern recognition system recognized approximately 30% of the images for the original features and 50% for the optimized features; however, a multi-layer perceptron (mlp) detected over 70% of the images with the optimized features. The research demonstrated the possibility of a future automotive navigation aid which robustly collects sign images and classifies these images in real-time with a single Fast Fourier Transform (FFT), a bank of filters and a trained neural net.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

Abstract: A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.