View Selection with Geometric Uncertainty Modeling

Estimating positions of world points from features observed in images is a key problem in 3D reconstruction, image mosaicking,simultaneous localization and mapping and structure from motion. We consider a special instance in which there is a dominant ground plane $\mathcal{G}$ viewed from a parallel viewing plane $\mathcal{S}$ above it. Such instances commonly arise, for example, in aerial photography. Consider a world point $g \in \mathcal{G}$ and its worst case reconstruction uncertainty $\varepsilon(g,\mathcal{S})$ obtained by merging \emph{all} possible views of $g$ chosen from $\mathcal{S}$. We first show that one can pick two views $s_p$ and $s_q$ such that the uncertainty $\varepsilon(g,\{s_p,s_q\})$ obtained using only these two views is almost as good as (i.e. within a small constant factor of) $\varepsilon(g,\mathcal{S})$. Next, we extend the result to the entire ground plane $\mathcal{G}$ and show that one can pick a small subset of $\mathcal{S'} \subseteq \mathcal{S}$ (which grows only linearly with the area of $\mathcal{G}$) and still obtain a constant factor approximation, for every point $g \in \mathcal{G}$, to the minimum worst case estimate obtained by merging all views in $\mathcal{S}$. Finally, we present a multi-resolution view selection method which extends our techniques to non-planar scenes. We show that the method can produce rich and accurate dense reconstructions with a small number of views. Our results provide a view selection mechanism with provable performance guarantees which can drastically increase the speed of scene reconstruction algorithms. In addition to theoretical results, we demonstrate their effectiveness in an application where aerial imagery is used for monitoring farms and orchards

Surveys of the earth's resources and environment by satellites

The potential and promise of observing the earth from the vantage point of space is discussed. The systematic surveying of processes and phenomena occurring on the surface of the earth by Landsat 1 and Nimbus 5 is considered to be useful in the following areas: assessment of water resources; mineral and petroleum exploration; land use planning; crop, forest, and rangeland inventory; assessment of flood, earthquake, and other environmental hazards; monitoring coastal processes; environmental effects of industrial effluents and of air pollution; mapping the distribution and types of ice covering the earth's polar caps and global soil moisture distributions

Maastiku ja visuaalse mõju hindamine Eesti tee-ehituse praktikas

Master’s Thesis Curriculum in Landscape ArchitectureThe aim of the thesis is to identify whether landscape and visual impact assessment is being used in road construction practice in Estonia. Turns out, it is not. The landscape and visual impact assessment carried out within the thesis, refers to rather significant effect on the landscape and its visual receptors. Although, evident generalisations could not be conducted, it can be stated that the landscape and visual impact should be assessed preceding or during road design process. However, the methodology should be adapted to meet the peculiarities of roads.Töö eesmärk on välja selgitada, kas Eesti tee-ehituse praktikas arvestatakse objektide mõjuga maastikule ning vaadetele. Töö käigus selgus, et seda ei tehta, peamiselt majanduslikel põhjustel, kuid meetodil võiks potentsiaali olla. Läbi viidud maastiku ja visuaalse mõju hindamised kinnitasid, et maantee objektidel on mõju maastikule ning see on kohati päris tõsine, ent üldistavaid järeldusi teha ei saa, mistõttu oleks metoodikat vaja praktika jaoks kohandada maanteede eripärasid arvestade

Bayesian Networks and Sex-related Homicides

We present a statistical investigation on the domain of sex-related homicides. As general sociological and psychological theory on this specific type of crime is incomplete or even lacking, a data-driven approach is implemented. In detail, graphical modelling is applied to learn the dependency structure and several structure learning algorithms are combined to yield a skeleton corresponding to distinct Bayesian Networks. This graph is subsequently analysed and presents a distinction between an offender and a situation driven crime.Bayesian Networks, structure learning, offender profiling