Classification non supervisée d’images 3D et extension à la segmentation exploitant les informations de couleur et de profondeur

Access to the 3D images at a reasonable frame rate is widespread now, thanks to the recent advances in low cost depth sensors as well as the efficient methods to compute 3D from 2D images. As a consequence, it is highly demanding to enhance the capability of existing computer vision applications by incorporating 3D information. Indeed, it has been demonstrated in numerous researches that the accuracy of different tasks increases by including 3D information as an additional feature. However, for the task of indoor scene analysis and segmentation, it remains several important issues, such as: (a) how the 3D information itself can be exploited? and (b) what is the best way to fuse color and 3D in an unsupervised manner? In this thesis, we address these issues and propose novel unsupervised methods for 3D image clustering and joint color and depth image segmentation. To this aim, we consider image normals as the prominent feature from 3D image and cluster them with methods based on finite statistical mixture models. We consider Bregman Soft Clustering method to ensure computationally efficient clustering. Moreover, we exploit several probability distributions from directional statistics, such as the von Mises-Fisher distribution and the Watson distribution. By combining these, we propose novel Model Based Clustering methods. We empirically validate these methods using synthetic data and then demonstrate their application for 3D/depth image analysis. Afterward, we extend these methods to segment synchronized 3D and color image, also called RGB-D image. To this aim, first we propose a statistical image generation model for RGB-D image. Then, we propose novel RGB-D segmentation method using a joint color-spatial-axial clustering and a statistical planar region merging method. Results show that, the proposed method is comparable with the state of the art methods and requires less computation time. Moreover, it opens interesting perspectives to fuse color and geometry in an unsupervised manner. We believe that the methods proposed in this thesis are equally applicable and extendable for clustering different types of data, such as speech, gene expressions, etc. Moreover, they can be used for complex tasks, such as joint image-speech data analysisL'accès aux séquences d'images 3D s'est aujourd'hui démocratisé, grâce aux récentes avancées dans le développement des capteurs de profondeur ainsi que des méthodes permettant de manipuler des informations 3D à partir d'images 2D. De ce fait, il y a une attente importante de la part de la communauté scientifique de la vision par ordinateur dans l'intégration de l'information 3D. En effet, des travaux de recherche ont montré que les performances de certaines applications pouvaient être améliorées en intégrant l'information 3D. Cependant, il reste des problèmes à résoudre pour l'analyse et la segmentation de scènes intérieures comme (a) comment l'information 3D peut-elle être exploitée au mieux ? et (b) quelle est la meilleure manière de prendre en compte de manière conjointe les informations couleur et 3D ? Nous abordons ces deux questions dans cette thèse et nous proposons de nouvelles méthodes non supervisées pour la classification d'images 3D et la segmentation prenant en compte de manière conjointe les informations de couleur et de profondeur. A cet effet, nous formulons l'hypothèse que les normales aux surfaces dans les images 3D sont des éléments à prendre en compte pour leur analyse, et leurs distributions sont modélisables à l'aide de lois de mélange. Nous utilisons la méthode dite « Bregman Soft Clustering » afin d'être efficace d'un point de vue calculatoire. De plus, nous étudions plusieurs lois de probabilités permettant de modéliser les distributions de directions : la loi de von Mises-Fisher et la loi de Watson. Les méthodes de classification « basées modèles » proposées sont ensuite validées en utilisant des données de synthèse puis nous montrons leur intérêt pour l'analyse des images 3D (ou de profondeur). Une nouvelle méthode de segmentation d'images couleur et profondeur, appelées aussi images RGB-D, exploitant conjointement la couleur, la position 3D, et la normale locale est alors développée par extension des précédentes méthodes et en introduisant une méthode statistique de fusion de régions « planes » à l'aide d'un graphe. Les résultats montrent que la méthode proposée donne des résultats au moins comparables aux méthodes de l'état de l'art tout en demandant moins de temps de calcul. De plus, elle ouvre des perspectives nouvelles pour la fusion non supervisée des informations de couleur et de géométrie. Nous sommes convaincus que les méthodes proposées dans cette thèse pourront être utilisées pour la classification d'autres types de données comme la parole, les données d'expression en génétique, etc. Elles devraient aussi permettre la réalisation de tâches complexes comme l'analyse conjointe de données contenant des images et de la parol

STK /WST 795 Research Reports

These documents contain the honours research reports for each year for the Department of Statistics.Honours Research Reports - University of Pretoria 20XXStatisticsBSs (Hons) Mathematical Statistics, BCom (Hons) Statistics, BCom (Hons) Mathematical StatisticsUnrestricte

Application of remote sensing to selected problems within the state of California

There are no author-identified significant results in this report

Analytical Techniques for the Improvement of Mass Spectrometry Protein Profiling

Bioinformatics is rapidly advancing through the "post-genomic" era following the sequencing of the human genome. In preparation for studying the inner workings behind genes, proteins and even smaller biological elements, several subdivisions of bioinformatics have developed. The subdivision of proteomics, concerning the structure and function of proteins, has been aided by the mass spectrometry data source. Biofluid or tissue samples are rapidly assayed for their protein composition. The resulting mass spectra are analyzed using machine learning techniques to discover reliable patterns which discriminate samples from two populations, for example, healthy or diseased, or treatment responders versus non-responders. However, this data source is imperfect and faces several challenges: unwanted variability arising from the data collection process, obtaining a robust discriminative model that generalizes well to future data, and validating a predictive pattern statistically and biologically.This thesis presents several techniques which attempt to intelligently deal with the problems facing each stage of the analytical process. First, an automatic preprocessing method selection system is demonstrated. This system learns from data and selects a combination of preprocessing methods which is most appropriate for the task at hand. This reduces the noise affecting potential predictive patterns. Our results suggest that this method can help adapt to data from different technologies, improving downstream predictive performance. Next, the issues of feature selection and predictive modeling are revisited with respect to the unique challenges posed by proteomic profile data. Approaches to model selection through kernel learning are also investigated. Key insights are obtained for designing the feature selection and predictive modeling portion of the analytical framework. Finally, methods for interpreting the resultsof predictive modeling are demonstrated. These methods are used to assure the user of various desirable properties: validation of the strength of a predictive model, validation of reproducible signal across multiple data generation sessions and generalizability of predictive models to future data. A method for labeling profile features with biological identities is also presented, which aids in the interpretation of the data. Overall, these novel techniques give the protein profiling community additional support and leverage to aid the predictive capability of the technology

Uncertainty in Hyperspectral Remote Sensing: Analysis of the Potential and Limitation of Shallow Water Bathymetry and Benthic Classification

Propagating the inherent uncertainty in hyperspectral remote sensing is key in understanding the limitation and potential of derived bathymetry and benthic classification. Using an improved optimisation algorithm, the potential of detecting temporal bathymetric changes above uncertainty was quantified from a time series of hyperspectral imagery. A new processing approach was also developed that assessed the limitations and potential of benthic classification by analysing optical separability of substrates above total system uncertainty and attenuating water column

Statistical study of solar radio bursts

The peak flux distribution of 40 years of solar radio burst data recorded by NOAA, as a function of frequency and time over a wide range of frequencies, was investigated to quantify the potential impact of radio bursts on wireless systems. Tables of fit parameters, which can be used to find burst occurrence rates in a number of frequency ranges, are presented. The typical power-law index of number density distribution, -1.8, is similar to that found in many hard X-ray studies. Significant changes were found in power-law index with frequency. The results may be useful for designers of current and future wireless systems. A set of 412 flares detected by OVSA during 2001-2002 was analyzed. Distributions of the spectral parameters were investigated and the correlations between them discussed, adding to the understanding of particle acceleration and flare energetics. The observed microwave bursts were classified according to their spectral and temporal characteristics, and the natural limits of different emission mechanisms were empirically inferred. An analysis of spectral peak evolution of microwave bursts found that, generally, the peak frequency is very well correlated with the intensity of microwave bursts, in agreement with theoretical predictions following from the gyrosynchrotron selfabsorption origin of the low frequency turnover of the spectrum. However, for some events the peak frequency increase is much smaller than expected or even absent. It was found that the peak frequency almost always increases on the late decay phase of strong long duration events. For 60% of the cases, the spectral dynamics of peak frequency around the peak time is caused by gyrosynchrotron self-absorption, but in 70% of cases, Razin suppression is dominant at some time during the burst, especially on the early rise and late decay phases. The properties of decimetric spike bursts occurring simultaneously with microwave gyrosynchrotron continuum bursts were analyzed. The spike cluster flux density was found to be tightly correlated with the high-frequency spectral index of the microwave continuum for each event, while the flux-to-flux correlation may not be present. It was concluded that the findings strongly support the electron cyclotron maser mechanism of spike emission

National Astronomy Meeting 2019 Abstract Book

The National Astronomy Meeting 2019 Abstract Book. Abstracts accepted and presented, including both oral and poster presentations, at the Royal Astronomical Society's NAM2019 conference, held at Lancaster University between 30 June and 4 July 2019