Improved terrain type classification using UAV downwash dynamic texture effect

The ability to autonomously navigate in an unknown, dynamic environment, while at the same time classifying various terrain types, are significant challenges still faced by the computer vision research community. Addressing these problems is of great interest for the development of collaborative autonomous navigation robots. For example, an Unmanned Aerial Vehicle (UAV) can be used to determine a path, while an Unmanned Surface Vehicle (USV) follows that path to reach the target destination. For the UAV to be able to determine if a path is valid or not, it must be able to identify the type of terrain it is flying over. With the help of its rotor air flow (known as downwash e↵ect), it becomes possible to extract advanced texture features, used for terrain type classification. This dissertation presents a complete analysis on the extraction of static and dynamic texture features, proposing various algorithms and analyzing their pros and cons. A UAV equipped with a single RGB camera was used to capture images and a Multilayer Neural Network was used for the automatic classification of water and non-water-type terrains by means of the downwash e↵ect created by the UAV rotors. The terrain type classification results are then merged into a georeferenced dynamic map, where it is possible to distinguish between water and non-water areas in real time. To improve the algorithms’ processing time, several sequential processes were con verted into parallel processes and executed in the UAV onboard GPU with the CUDA framework achieving speedups up to 10x. A comparison between the processing time of these two processing modes, sequential in the CPU and parallel in the GPU, is also presented in this dissertation. All the algorithms were developed using open-source libraries, and were analyzed and validated both via simulation and real environments. To evaluate the robustness of the proposed algorithms, the studied terrains were tested with and without the presence of the downwash e↵ect. It was concluded that the classifier could be improved by per forming combinations between static and dynamic features, achieving an accuracy higher than 99% in the classification of water and non-water terrain.Dotar equipamentos moveis da funcionalidade de navegação autónoma em ambientes desconhecidos e dinâmicos, ao mesmo tempo que, classificam terrenos do tipo água e não água, são desafios que se colocam atualmente a investigadores na área da visão computacional. As soluções para estes problemas são de grande interesse para a navegação autónoma e a colaboração entre robôs. Por exemplo, um veículo aéreo não tripulado (UAV) pode ser usado para determinar o caminho que um veículo terrestre não tripulado (USV) deve percorrer para alcançar o destino pretendido. Para o UAV conseguir determinar se o caminho é válido ou não, tem de ser capaz de identificar qual o tipo de terreno que está a sobrevoar. Com a ajuda do fluxo de ar gerado pelos motores (conhecido como efeito downwash), é possível extrair características de textura avançadas, que serão usadas para a classificação do tipo de terreno. Esta dissertação apresenta uma análise completa sobre extração de texturas estáticas e dinâmicas, propondo diversos algoritmos e analisando os seus prós e contras. Um UAV equipado com uma única câmera RGB foi usado para capturar as imagens. Para classi ficar automaticamente terrenos do tipo água e não água foi usada uma rede neuronal multicamada e recorreu-se ao efeito de downwash criado pelos motores do UAV. Os re sultados da classificação do tipo de terreno são depois colocados num mapa dinâmico georreferenciado, onde é possível distinguir, em tempo real, terrenos do tipo água e não água. De forma a melhorar o tempo de processamento dos algoritmos desenvolvidos, vários processos sequenciais foram convertidos em processos paralelos e executados na GPU a bordo do UAV, com a ajuda da framework CUDA, tornando o algoritmo até 10x mais rápido. Também são apresentadas nesta dissertação comparações entre o tempo de processamento destes dois modos de processamento, sequencial na CPU e paralelo na GPU. Todos os algoritmos foram desenvolvidos através de bibliotecas open-source, e foram analisados e validados, tanto através de ambientes de simulação como em ambientes reais. Para avaliar a robustez dos algoritmos propostos, os terrenos estudados foram testados com e sem a presença do efeito downwash. Concluiu-se que o classificador pode ser melhorado realizando combinações entre as características de textura estáticas e dinâmicas, alcançando uma precisão superior a 99% na classificação de terrenos do tipo água e não água

Covariate construction of nonconvex windows for spatial point pattern data

Mini Dissertation (MSc (Mathematical Statistics))--University of Pretoria, 2020In the field of spatial statistics, window selection for point pattern data is a complex process. In some cases, the point pattern window is given a priori when a local phenomena is studied. In other cases, a researcher may choose this region using some objective means that reflects their view that the window may be representative of a larger region, or based on a probability sampling method. The common approaches used are the smallest rectangular bounding window and convex windows due to the obvious use of the Euclidean distance. The chosen window must however cover the true domain of the sampled point pattern data. Choosing a window too large results in estimation and inference in areas which are empty of observed data, but for which it has not been confirmed that observations could have occurred there. These holes in the domain could be regions where for some geographic (or other) reason the phenomena of interest does not occur. In this mini-dissertation a review of methods for spatial convex and nonconvex window estimation is provided, and an algorithm is proposed for selecting the point pattern domain without the restriction of convexity, allowing for a better fit to the true domain, and based on spatial covariate information. The effect of the window choice on spatial intensity estimates is illustrated by giving particular attention to the technique of smoothed kernel intensity estimation. The proposed algorithm is applied in the setting of rural villages in Tanzania's Mara province. As a spatial covariate, remotely sensed data based on the elevation of a point pattern is used in the form of a Digital Elevation Model (DEM) GTOPO30, specific to village house locations in this setting. Mathematical morphological operators are also used to extract physiographic features from the DEM and are included here as a preprocessing step in the spatial window domain modelling.STATOMET, DST/NRF SARChI ChairStatisticsMSc (Mathematical Statistics)Unrestricte

Voronoi tessellation‐based regionalised segmentation for colour texture image

This study presents a region‐based algorithm for segmenting colour texture image, which uses Voronoi tessellation for partitioning the domain of the image and Markov random field (MRF) for modelling colour texture. In detail, (i) an image domain is divided into polygons (or sub‐regions) by Voronoi tessellation; (ii) two MRF models, improved Potts model and multivariate Gaussian MRF model, are used to characterise colour texture structures inter‐ and intra‐polygons, respectively; (iii) by Bayesian paradigm, a posterior distribution which characterises the segmentation and model parameters conditional on a given colour image can be obtained up to a normalising constant; (iv) a Markov chain Monte Carlo algorithm is developed to simulate from the posterior distribution; finally, (v) a maximum a posteriori scheme is employed to find an optimal segmentation and model parameters. In order to evaluate the proposed colour texture segmentation algorithm, two kinds of colour texture images are tested, including synthetic and real colour texture images. The accuracy assessments are performed qualitatively on all kinds of images and quantitatively on synthetic images. All results demonstrate that the proposed algorithm is efficiently

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data