OF THE DISSERTATION Robust Statistics over Riemannian Manifolds for Computer Vision

Official statistics has not properly researched and understood how its methods models behave at times of downturns (and potentially in the corresponding situation similarly paced (unpredictable fast) growths). There is generally a wish to make robust unusual changes, but these are often tackled by situation. Production official during COVID-19 necessitated some radical changes both data collect...

In many situations, data follow a generalized linear model in which the mean of the responses is modelled, through a link function, linearly on the covariates. In this paper, robust estimators for the regression parameter are considered in order to build test statistics for this parameter when missing data occur in the responses. We derive the asymptotic behaviour of the robust estimators for t...

A novel method for estimating the variance and standard deviation of the additive white Gaussian noise contained in an image will be presented. Only a single image is used to estimate the noise properties. Local image outliers are discarded, this allows us to separate the additive zero mean white Gaussian noise contained in a noisy image from the original image structure. Local variance estimat...

Discontinuity adaptive MRF priors have been used for modeling vision problems involving discontinuities and robust statistics models for solving regression problems involving outliers. This paper presents a comparative study of the two kinds of models. We analyze the mechanisms of adaptation (to discontinuities) and robustness (to outliers) and give a necessary condition for the adaptation and ...

In practical scenarios for speaker adaptation of speech synthesis systems, the quality of adaptation audio data may be poor. In these situations, it is necessary to make use of the available audio to capture the speaker attributes, whilst aiming to obtain a synthesis voice which does not have any of the lowquality attributes of the audio. One approach to achieving this is to define a sub-space ...