Interactive Vegetation Rendering with Slicing and Blending

Detailed and interactive 3D rendering of vegetation is one of the challenges of traditional polygon-oriented computer graphics, due to large geometric complexity even of simple plants. In this paper we introduce a simplified image-based rendering approach based solely on alpha-blended textured polygons. The simplification is based on the limitations of human perception of complex geometry. Our approach renders dozens of detailed trees in real-time with off-the-shelf hardware, while providing significantly improved image quality over existing real-time techniques. The method is based on using ordinary mesh-based rendering for the solid parts of a tree, its trunk and limbs. The sparse parts of a tree, its twigs and leaves, are instead represented with a set of slices, an image-based representation. A slice is a planar layer, represented with an ordinary alpha or color-keyed texture; a set of parallel slices is a slicing. Rendering from an arbitrary viewpoint in a 360 degree circle around the center of a tree is achieved by blending between the nearest two slicings. In our implementation, only 6 slicings with 5 slices each are sufficient to visualize a tree for a moving or stationary observer with the perceptually similar quality as the original model

Interaktivna interakcijska analiza

Interakcije lahko razumemo kot korelacije, ki obsegajo več kot le dva atributa. Neka skupina atributov je med seboj v interakciji, če njihovih medsebojnih povezanosti ne moremo popolnoma razumeti, ne da bi jih vse opazovali hkrati. Interakcije so zakonitosti skupin več atributov. V tem članku merimo pomembnost interakcije s postopki, ki temeljijo na Shannonovi entropiji kot pojmu negotovosti, ki je bolj splošen od koncepta statistične variance. Cilj interakcijske analize je analitiku predstaviti interakcije grafično z več tipi diagramov. S tem namenom smo izdelali orodja, ki omogočajo interaktivno preučevanje podatkov in nudijo pomoč pri iskanju zanimivih pogledov na podatke. Interakcije prinašajo tudi nov pogled na nekatere težave postopkov strojnega učenja

Strojno učenje s simplicialnimi kompleksi

Cilj tega besedila je prikazati in upravičiti uporabo matematične strukture simplicialnih kompleksov v strojnem učenju. V tem okviru predstavimo metodo strojnega učenja, ki hkrati kombinira lokalnost in nehierarhičnost in ki lahko učinkovito deluje z zveznimi atributi. Strojno učenje hipotez, predstavljenih s simplicialnimi kompleksi, izpolnjuje te pogoje in zato odpira nekatera nova področja uporabe, na primer pomoč pri vizualizaciji podatkov. Ker so simplicialni kompleksi matematični objekti, v besedilu predstavimo matematično ozadje, ki sega v topologijo, linearno algebro in do neke mere v računsko geometrijo. V nadaljevanju opišemo osnovne postopke za izgradnjo klasifikacijskih hipotez in klasifikacijo ter značilnosti same implementacije. Metodo je mogoče prilagoditi za obravnavanje šuma ter jo integrirati z drugimi metodami, na primer z dekompozicijo. Prikažemo tudi načine pohitritve uporabljenih algoritmov. Končamo s kratkim pregledom prednosti in slabosti postopka. V dodatku predstavimo še aplikacijo CCW, ki uporabniku omogoča enostavno vizualno delo s klasifikatorji na podlagi simplicialnih kompleksov v dveh dimenzijah, kar ilustriramo z več primeri in s preizkusom uspešnosti

Attribute Interactions in Medical Data Analysis

There is much empirical evidence about the success of naive Bayesian classification (NBC) in medical applications of attribute-based machine learning. NBC assumes conditional independence between attributes. In classification, such classifiers sum up the pieces of class-related evidence from individual attributes, independently of other attributes. The performance, however, deteriorates significantly when the “interactions” between attributes become critical. We propose an approach to handling attribute interactions within the framework of “voting” classifiers, such as NBC. We propose an operational test for detecting interactions in learning data and a procedure that takes the detected interactions into account while learning. This approach induces a structuring of the domain of attributes, it may lead to improved classifier’s performance and may provide useful novel information for the domain expert when interpreting the results of learning. We report on its application in data analysis and model construction for the prediction of clinical outcome in hip arthroplasty

Information and Symmetry

Before information theory can be applied, we must postulate a particular model of the universe based on probability theory. We journey through the assumptions, advantages and disadvantages of the view. There are three kinds of symmetry or similarity in such a universe: symmetries between probabilities reveal ignorance, symmetries between events reveal indifference, and symmetries between properties reveal information

Modelling Modelled

A model is one of the most fundamental concepts: it is a formal and generalized explanation of a phenomenon. Only with models we can bridge the particulars and predict the unknown. Virtually all our intellectual work turns around finding models, evaluating models, using models. Because models are so pervasive, it makes sense to take a look at modelling itself. We will approach this problem, of course, by building a model of the process of modelling

Information-Theoretic Exploration and Evaluation of Models

No information-theoretic quantity, such as entropy or Kullback-Leibler divergence, is meaningful without first assuming a probabilistic model. In Bayesian statistics, the model itself is uncertain, so the resulting information-theoretic quantities should also be treated as uncertain. Information theory provides a language for asking meaningful decision-theoretic questions about black-box probabilistic models, where the chosen utility function is log-likelihood. We show how general hypothesis testing can be developed from these conclusions, also handling the problem of multiple comparisons. Furthermore, we use mutual and interaction information to disentangle and visualize the structure inside black-box probabilistic models. On examples we show how misleading can non-generative models be about informativeness of attributes