Multiwavelet analysis and signal processing

Cotronei, Mariantonia; Montefusco, Laura; Puccio, Luigia

doi:10.1109/82.718807

Cited by 92 publications

(55 citation statements)

References 14 publications

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“…One might also apply zerotree-coding methods [32] in a multiwavelet context. Other interesting results on implementation of multiwavelets for image compression can be found in [7]. …”

Section: Transform-based Image Codingmentioning

confidence: 99%

The application of multiwavelet filterbanks to image processing

Strela

Heller

Strang

et al. 1999

IEEE Trans. on Image Process.

376

213

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Multiwavelets are a new addition to the body of wavelet theory. Realizable as matrix-valued filter banks leading to wavelet bases, multiwavelets offer simultaneous orthogonality, symmetry, and short support, which is not possible with scalar 2-channel wavelet systems. After reviewing this recently developed theory, we examine the use of multiwavelets in a filter bank setting for discrete-time signal and image processing. Multiwavelets differ from scalar wavelet systems in requiring two or more input streams to the multiwavelet filter bank. We describe two methods (repeated row and approximation/deapproximation) for obtaining such a vector input stream from a one-dimensional signal. Algorithms for symmetric extension of signals at boundaries are then developed, and naturally integrated with approximation-based preprocessing. We describe an additional algorithm for multiwavelet processing of two-dimensional signals, two rows at a time, and develop a new family of multiwavelets (the constrained pairs) that is well-suited to this approach. This suite of novel techniques is then applied to two basic signal processing problems, denoising via wavelet-shrinkage, and data compression. After developing the approach via model problems in one dimension, we applied multiwavelet processing to images, frequently obtaining performance superior to the comparable scalar wavelet transform.

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“…One might also apply zerotree-coding methods [32] in a multiwavelet context. Other interesting results on implementation of multiwavelets for image compression can be found in [7]. …”

Section: Transform-based Image Codingmentioning

confidence: 99%

The application of multiwavelet filterbanks to image processing

Strela

Heller

Strang

et al. 1999

IEEE Trans. on Image Process.

376

213

View full text Add to dashboard Cite

show abstract

“…It offers many theoretical and experimental advantages. For example, multiwavelets have been constructed to simultaneously possess symmetry, orthogonality, and compact support [104][105][106][107][108]. The multiwavelets have some unique characteristics that cannot be obtained with scalar wavelets.…”

Section: Multiwavelet Transformmentioning

confidence: 99%

Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signals

Bajaj

Pachori

2014

Intelligent Systems Reference Library

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Emotion classification based on electroencephalogram (EEG) signals is a relatively new area of research in the development of brain computer interface (BCI) system with challenging issues like induction of the emotional states and the extraction of the features in order to obtain optimum classification of human emotions. The emotion classification system based on BCI can be useful in many areas like as entertainment, education, and health care. This chapter presents a new method for human emotion classification using multiwavelet transform of EEG signals. The EEG signal contains useful information related to the different emotional states, which helps us to understand the psychology and neurology of the human brain. The features namely, ratio of the norms based measure, Shannon entropy measure, and normalized Renyi entropy measure are computed from the sub-signals generated by multiwavelet decomposition of EEG signals. These features have been used as an input to multiclass least squares support vector machine (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for classification of human emotions from EEG signals. The classification performance of the proposed method for classification of emotions using EEG signals determined by computing the classification accuracy, ten-fold crossvalidation, and confusion matrix. The proposed method has provided classification accuracy of 84.79 % for classification of human emotions namely happy, neutral, sadness, and fear from EEG signals with Morlet wavelet kernel function of MC-LS-SVM. The audio-video stimulus has been used for inducing the emotions in EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of human emotions from EEG signals.

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“…The main advantage of using multiwavelet is that it is possible to construct multiwavelet bases possessing several desirable properties at the same time, for example orthogonality, symmetry, short support and a high number of vanishing moments (Cotronei et al, 1998;Strela et al, 1999;Geronimo et al, 1994;Xia et al, 1996;Hardin and Roach, 1998). Recently, a few works of multiwavelet watermarking were reported.…”

Section: Introductionmentioning

confidence: 99%