Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics programs consist of a stochastic scene generator, a renderer based on graphics software, a stochastic likelihood model linking the renderer's output and the data, and latent variables that adjust the fidelity of the renderer and the tolerance of the likelihood model. Representations and algorithms from computer graphics, originally designed to produce high-quality images, are instead used as the deterministic backbone for highly approximate and stochastic generative models. This formulation combines probabilistic programming, computer graphics, and approximate Bayesian computation, and depends only on general-purpose, automatic inference techniques. We describe two applications: reading sequences of degraded and adversarially obscured alphanumeric characters, and inferring 3D road models from vehicle-mounted camera images. Each of the probabilistic graphics programs we present relies on under 20 lines of probabilistic code, and supports accurate, approximately Bayesian inferences about ambiguous real-world images.Comment: The first two authors contributed equally to this wor

The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

Avtonomna segmentacija slik z Markovim slučajnim poljem

Segmentacija slik je zelo raziskovano področje, za katero so na voljo številni algoritmi. Naš cilj je segmentacija slike s pomočjo superpikslov na več skladnih delov in na nenadzorovan način. Da bi to dosegli, predlagamo iterativni segmentacijski algoritem. Algoritem predstavlja sliko kot slučajno polje Markova (MRF), katerega vozlišča so superpiksli, ki imajo barvne in teksturne atribute. Superpikslom dodelimo oznake na podlagi njihovih atributov s pomočjo metode podpornih vektorjev (SVM) in že omenjenega MRF in iterativno zmanjšujemo število segmentov. Negotovo segmentacijo po vsaki iteraciji se izboljšuje in rezultat je segmentacija slike na več semantično smiselnih delov, brez pomoči uporabnika. Algoritem je bil testiran na segmentacijsko podatkovno bazo in F ocene so podobne najsodobnejšim algoritmom. Glede fragmentacije slike naš pristop bistveno prekosi stanje tehnike z zmanjšanjem števila segmentov, iz katerih je sestavljen predmet zanimanja.Image segmentation is a widely-researched topic with many algorithms available. Our goal is to segment an image, in an unsupervised way, into several coherent parts with the help of superpixels. To achieve that, we propose an iterative segmentation algorithm. The algorithm models the image by a Markov random field, whose nodes are the superpixels, and each node has both color and texture features. The superpixels are assigned labels according to their features with the help of support vector machines and the aforementioned MRF and the number of segments is iteratively reduced. The result is a segmentation of an image into several regions with requiring any user input. The segmentation algorithm was tested on a standard evaluation database, and performs on par with state-of-the-art segmentation algorithms in F-measures. In terms of oversegmentation, our approach significantly outperforms the state of the art by greatly reducing the oversegmentation of the object of interest

Understanding the twist-bend nematic phase: the characterisation of 1-(4-cyanobiphenyl-4'-yloxy)-6-(4-cyanobiphenyl-4'--yl)hexane (CB6OCB) and comparison with CB7CB

The synthesis and characterisation of the nonsymmetric liquid crystal dimer, 1-(4-cyanobiphenyl-40-yloxy)-6- (4-cyanobiphenyl-40-yl)hexane (CB6OCB) is reported. An enantiotropic nematic (N)–twist-bend nematic (NTB) phase transition is observed at 109 1C and a nematic–isotropic phase transition at 153 1C. The NTB phase assignment has been confirmed using polarised light microscopy, freeze fracture transmission electron microscopy (FFTEM), 2H-NMR spectroscopy, and X-ray diffraction. The effective molecular length in both the NTB and N phases indicates a locally intercalated arrangement of the molecules, and the helicoidal pitch length in the NTB phase is estimated to be 8.9 nm. The surface anchoring properties of CB6OCB on a number of aligning layers is reported. A Landau model is applied to describe high-resolution heat capacity measurements in the vicinity of the NTB–N phase transition. Both the theory and heat capacity measurements agree with a very weak first-order phase transition. A complementary extended molecular field theory was found to be in suggestive accord with the 2H-NMR studies of CB6OCB-d2, and those already known for CB7CB-d4. These include the reduced transition temperature, TNTBN/TNI, the order parameter of the mesogenic arms in the N phase close to the NTB–N transition, and the order parameter with respect to the helix axis which is related to the conical angle for the NTB phase.Postprint (published version

Partially ordered models

We provide a formal definition and study the basic properties of partially ordered chains (POC). These systems were proposed to model textures in image processing and to represent independence relations between random variables in statistics (in the later case they are known as Bayesian networks). Our chains are a generalization of probabilistic cellular automata (PCA) and their theory has features intermediate between that of discrete-time processes and the theory of statistical mechanical lattice fields. Its proper definition is based on the notion of partially ordered specification (POS), in close analogy to the theory of Gibbs measure. This paper contains two types of results. First, we present the basic elements of the general theory of POCs: basic geometrical issues, definition in terms of conditional probability kernels, extremal decomposition, extremality and triviality, reconstruction starting from single-site kernels, relations between POM and Gibbs fields. Second, we prove three uniqueness criteria that correspond to the criteria known as bounded uniformity, Dobrushin and disagreement percolation in the theory of Gibbs measures.Comment: 54 pages, 11 figures, 6 simulations. Submited to Journal of Stat. Phy

A Study of the Texture Measurement Definition Problem (Image Analysis).

The problem of texture measurement definition is studied. A theory is presented in order to overcome the problem. This theory includes a conceptual framework for the general measurement definition problem. This framework is based on the concepts of a perceptual transform, a perceptual space, a measurement transform and a similarity transform. Using this conceptual framework and a particular choice for the similarity transform, a set of requirements are defined. These requirements can be used to create a formal method for defining measurements off the GLC matrices. The formal procedure is based on preserving perceptual relationships among textures. To apply this technique a perceptual norm, a least-squares procedure and a synthesis procedure are needed. Each of these components are investigated and the motivation for selecting each of the methods used is presented. Individually, these components are tested in order to see if they are appropriate for use with our technique. Finally, a feasibility study is discussed to demonstrate the possibility of using this technique to solve for measurements. The problem was to define measurements given a limited number of textures. The defined measurements are studied in order to establish their contributions. It is shown that the newly defined measurements are gauging the periodicity and the symmetry of patterns, perceptual entities that have been demonstrated to be important in the human vision system