Use Of Wavelet Coefficients Research Articles

Wavelet Multidimensional Scaling Analysis of European Economic Sentiment Indicators

We propose the use of wavelet coefficients, which are generated from nondecimated discreet wavelet transforms, to form a correlation-based dissimilarity measure in metric multidimensional scaling. This measure enables the construction of configurations depicting the associations between objects across different timescales. The proposed method is used to examine the similarities between the economic sentiment indicators of the EU member states that are published monthly by the European Commission. The results suggest that economic sentiment differs considerably among the member states in the short term. In contrast, several similarities emerge when considering the associations over longer time horizons. These similarities tend to be related to the countries that are geographically close or that exhibited similar economic behaviour prior to the introduction of the euro. Furthermore, the results of a detailed simulation study suggest that the proposed dissimilarity measure is particularly well suited for identifying long-term associations between nonstationary time series.

A Deep Learning Interpretable Model for Novel Coronavirus Disease (COVID-19) Screening with Chest CT Images

In this article, we propose a convolutional neural network (CNN)-based model, a ResNet-50 based model, for discriminating coronavirus disease 2019 (COVID-19) from Non-COVID-19 using chest CT. We adopted the use of wavelet coefficients of the entire image without cropping any parts of the image as input to the CNN model. One of the main contributions of this study is to implement an algorithm called gradient-weighted class activation mapping to produce a heat map for visually verifying where the CNN model is looking at the image, thereby, ensuring the model is performing correctly. In order to verify the effectiveness and usefulness of the proposed method, we compare the obtained results with that obtained by using pixel values of original images as input to the CNN model. The measures used for performance evaluation include accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and Matthews correlation coefficient (MCC). The overall classification accuracy, F1 score, and MCC for the proposed method (using wavelet coefficients as input) were 92.2%, 0.915%, and 0.839%, and those for the compared method (using pixel values of the original image as input) were 88.3%, 0.876%, and 0.766%, respectively. The experiment results demonstrate the superiority of the proposed method. Moreover, as a comprehensible classification model, the interpretability of classification results was introduced. The region of interest extracted by the proposed model was visualized using heat maps and the probability score was also shown. We believe that our proposed method could provide a promising computerized toolkit to help radiologists and serve as a second eye for them to classify COVID-19 in CT scan screening examination.

Application of Machine Learning Methods for Biometric Identification by Iris

The paper is devoted to the application of machine learning methods for biometric identification of the iris. for biometric identification of a person by iris recognition. In order to investigate the possibilities of machine learning for the iris recognition, the UBIRIS database was used in this work. A distinctive feature of this database is that the task of identification is complicated by the registration of images in non-ideal conditions. This approach is aimed at developing algorithms for automatic iris recognition under limited conditions for obtaining high-quality images, remote registration, noise, and in the absence of cooperation by the subject. Glare from light sources, differences in lighting conditions, closure of part of the eye by the eyelids complicate the recognition process due to the significant fluctuation of features for different images of the iris of one person. The purpose of this work is to identify and compare the informative features of the iris obtained in different coordinate bases, as well as to choose the classification method that provides the highest accuracy in the identification of the person on the iris. Comparison of the use in iris recognition such features as the coefficients of two-dimensional discrete Fourier transform, the coefficients of discrete cosine transform, as well as the approximation coefficients of the 3rd and 4th levels of two-dimensional wavelet transform is considered. The accuracy of a number of classifiers using these feature sets is determined. Based on this, a combination of features and classifiers are proposed that provides the highest accuracy of person identification based on the iris recognition. The highest classification accuracy of the iris recognition was 98.5% using the approximation coefficients of the 4th level of two-dimensional wavelet decomposition and a classifier implementing the linear discriminant method. The use of two-dimensional discrete Fourier transform coefficients for iris recognition yielded 97.5% correctly identified individuals in the case of machine learning method based on the k-nearest neighbor ensemble. This algorithm also made it possible to obtain maximum classification accuracy (94.5%) for the coefficients of the discrete cosine transformation. The use of wavelet coefficients can significantly reduce the dimensionality of the data for classification compared to the use of Fourier transform coefficients or discrete cosine transform. Applying of vertical, horizontal and diagonal wavelet coefficients of detail as features for iris recognition gives lower accuracy compared to the use of wavelet coefficients of approximation of the same level.

Direct Visual Servoing Using Wavelet Coefficients

This paper focuses on the design of a 6 degrees of freedom visual servoing control law. Instead of using geometric visual features in standard vision-based approaches, the proposed controller makes use of wavelet coefficients as control signal inputs. It uses the multiple resolution coefficients representing the wavelet transform of an image in the spatial domain. The main contributions are the definition of the multiple resolution wavelet interaction model that links the time-variation of wavelet coefficients to the robot spatial velocity and the associated task function controller. The proposed control law was tested and validated experimentally using a commercial micromanipulator in an eye-to-hand configuration. To be able to judge the efficiency of the control law, several validation tests were carried out under different conditions of use i.e., large illumination variations, noisy images, partial occlusions, and using unknown three-dimensional scenes. It is also demonstrated experimentally that the proposed approach outperforms, the well-known photometry visual servoing as well as a feature-based visual servoing, namely in unfavorable conditions of use.

Fast triangular mesh approximation of surface data using wavelet coefficients

This paper proposes a new, fast, triangular mesh approximation method for the 3D visualization of surface data. Using spatiofrequency localization characteristics and directional information of wavelet coefficients, we determine local complexities of surface data and approximate the data to a proper triangular mesh. The proposed algorithm is quite simple, and the computational cost is low due to the direct use of wavelet coefficients for vertex removal. The computer simulation results for terrain data show that the proposed algorithm is excellent for fast 3D visualization.