Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination

We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure

Multivariate texture discrimination based on geodesics to class centroids on a generalized Gaussian Manifold

A texture discrimination scheme is proposed wherein probability distributions are deployed on a probabilistic manifold for modeling the wavelet statistics of images. We consider the Rao geodesic distance (GD) to the class centroid for texture discrimination in various classification experiments. We compare the performance of GD to class centroid with the Euclidean distance in a similar context, both in terms of accuracy and computational complexity. Also, we compare our proposed classification scheme with the k-nearest neighbor algorithm. Univariate and multivariate Gaussian and Laplace distributions, as well as generalized Gaussian distributions with variable shape parameter are each evaluated as a statistical model for the wavelet coefficients. The GD to the centroid outperforms the Euclidean distance and yields superior discrimination compared to the k-nearest neighbor approach

Advanced Techniques for Computational and Information Sciences

New techniques in computational and information sciences have played an important role in keeping advancing the so called knowledge economy. Advanced techniques have been introduced to or emerging in almost every field of the scientific world for hundreds of years, which has been accelerated since the late 1970s when the advancement in computers and digital technologies brought the world into the Information Era. In addition to the rapid development of computational intelligence and new data fusion techniques in the past thirty years [1–4], mobile and cloud computing, grid computing driven numeric computation models, big data intelligence, and other emerging technologies have not only expanded the scope of traditional simulation and modelling in many scientific and engineering disciplines [5–8] but also enabled the fusion of traditional and contemporary methods in almost every field in the world [9–11]

Соціально-педагогічна діяльність щодо формування психолого-педагогічної культури батьків в умовах ЗНЗ

A procedure is developed to quantify and improve the signal-to-noise ratio (SNR) of Magnetic Resonance images. The image SNR is quanti ed using the correlation function of two independent acquisitions of an image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. The proposed correlation technique is also used to improve the SNR by estimating the amplitude of the signal spectrum. The technique is applied to a set of MR images and its performance, in terms of gain in SNR, contrast-to-noise ratio (CNR) and resolution loss, is compared to that of classical noise filters. The SNR as well as the CNR is found to be improved signi cantly with minor loss of resolution. Finally, it is shown that the correlation technique can be implemented in a highly efficient way in almost any acquisition procedure of

Advanced concepts for intelligent vision systems, 19th international conference, ACIVS 2018, proceedings

International audienceThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2018, held in Poitiers, France, in September 2018. The 52 full papers presented in this volume were carefully reviewed and selected from 91 submissions. They were organized in topical sections named: video analysis; segmentation and classification; remote sending; biometrics; deep learning; coding and compression; and image restauration and reconstruction