Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding

Adequate Inner Bound for Geometric Modeling with Compact Field Function

International audienceRecent advances in implicit surface modeling now provide highly controllable blending effects. These effects rely on the field functions of $\mathbb{R}^3 \rightarrow \mathbb{R}$ in which the implicit surfaces are defined. In these fields, there is an outside part in which blending is defined and an inside part. The implicit surface is the interface between these two parts. As recent operators often focus on blending, most efforts have been made on the outer part of field functions and little attention has been paid on the inner part. Yet, the inner fields are important as soon as difference and intersection operators are used. This makes its quality as crucial as the quality of the outside. In this paper, we analyze these shortcomings, and deduce new constraints on field functions such that differences and intersections can be seamlessly applied without introducing discontinuities or field distortions. In particular, we show how to adapt state of the art gradient-based union and blending operators to our new constraints. Our approach enables a precise control of the shape of both the inner or outer field boundaries. We also introduce a new set of asymmetric operators tailored for the modeling of fine details while preserving the integrity of the resulting fields

A Gradient-Based Implicit Blend

International audienceWe introduce a new family of binary composition operators that solves four major problems of constructive implicit modeling: suppressing bulges when two shapes merge, avoiding unwanted blending at a distance, ensuring that the resulting shape keeps the topology of the union, and enabling sharp details to be added without being blown up. The key idea is that field functions should not only be combined based on their values, but also on their gradients.We implement this idea through a family of C1 composition operators evaluated on the GPU for efficiency, and illustrate it by applications to constructive modeling and animation

Computer Graphics Learning Materials

Selles lõputöös on antud ülevaade Tartu Ülikooli aine Arvutigraafika (MTAT.03.015) jaoks koostatud õppematerjalist ja õppekeskkonnast. Kirjeldatud on aine modulaarset ülesehitust, mis rakendab kombineeritud ülevalt-alla (ing. k. top-down) ja alt-üles (ing. k. bottom-up) lähenemisi. Loodud õppematerjal sisaldab endas interaktiivseid näiteid, mis vastavad hõivatuse taksonoomia 4ndale tasemele. Õppekeskkonna CGLearn spetsifikatsioon ja implementatsiooni detailid on kirjeldatud. Töö lõpus on kursusel osalenud õpilaste hulgas läbi viidud tagasiside küsitluse tulemuste analüüsiga. Lisa fail on lingina kätesaadav serveri probleemide tõttu aadresil : http://comserv.cs.ut.ee/forms/ati_report/files/ComputerGraphicsLearningMaterialsAppendix.zipThis thesis provides an overview of the learning material and a custom learning environment created for the Computer Graphics (MTAT.03.015) course in the University of Tartu. It describes a modular layout, that mixes a top-down and bottom-up approaches, in which the course was organized. The created material also includes interactive examples that satisfy engagement level 4 requirements. The specification and implementation details of the custom learning environment called CGLearn are given. Thesis concludes with the analysis of the feedback questionnaire answered by the students participating in the course and using the material. Due to server problems extras file is in here : http://comserv.cs.ut.ee/forms/ati_report/files/ComputerGraphicsLearningMaterialsAppendix.zi

High-performance geometric vascular modelling

Image-based high-performance geometric vascular modelling and reconstruction is an essential component of computer-assisted surgery on the diagnosis, analysis and treatment of cardiovascular diseases. However, it is an extremely challenging task to efficiently reconstruct the accurate geometric structures of blood vessels out of medical images. For one thing, the shape of an individual section of a blood vessel is highly irregular because of the squeeze of other tissues and the deformation caused by vascular diseases. For another, a vascular system is a very complicated network of blood vessels with different types of branching structures. Although some existing vascular modelling techniques can reconstruct the geometric structure of a vascular system, they are either time-consuming or lacking sufficient accuracy. What is more, these techniques rarely consider the interior tissue of the vascular wall, which consists of complicated layered structures. As a result, it is necessary to develop a better vascular geometric modelling technique, which is not only of high performance and high accuracy in the reconstruction of vascular surfaces, but can also be used to model the interior tissue structures of the vascular walls.This research aims to develop a state-of-the-art patient-specific medical image-based geometric vascular modelling technique to solve the above problems. The main contributions of this research are:- Developed and proposed the Skeleton Marching technique to reconstruct the geometric structures of blood vessels with high performance and high accuracy. With the proposed technique, the highly complicated vascular reconstruction task is reduced to a set of simple localised geometric reconstruction tasks, which can be carried out in a parallel manner. These locally reconstructed vascular geometric segments are then combined together using shape-preserving blending operations to faithfully represent the geometric shape of the whole vascular system.- Developed and proposed the Thin Implicit Patch method to realistically model the interior geometric structures of the vascular tissues. This method allows the multi-layer interior tissue structures to be embedded inside the vascular wall to illustrate the geometric details of the blood vessel in real world

Painterly rendering techniques: A state-of-the-art review of current approaches

In this publication we will look at the different methods presented over the past few decades which attempt to recreate digital paintings. While previous surveys concentrate on the broader subject of non-photorealistic rendering, the focus of this paper is firmly placed on painterly rendering techniques. We compare different methods used to produce different output painting styles such as abstract, colour pencil, watercolour, oriental, oil and pastel. Whereas some methods demand a high level of interaction using a skilled artist, others require simple parameters provided by a user with little or no artistic experience. Many methods attempt to provide more automation with the use of varying forms of reference data. This reference data can range from still photographs, video, 3D polygonal meshes or even 3D point clouds. The techniques presented here endeavour to provide tools and styles that are not traditionally available to an artist. Copyright © 2012 John Wiley & Sons, Ltd

Diverse perceptions of smart spaces

This is the era of smart technology and of ‘smart’ as a meme, so we have run three workshops to examine the ‘smart’ meme and the exploitation of smart environments. The literature relating to smart spaces focuses primarily on technologies and their capabilities. Our three workshops demonstrated that we require a stronger user focus if we are advantageously to exploit spaces ascribed as smart: we examined the concept of smartness from a variety of perspectives, in collaboration with a broad range of contributors. We have prepared this monograph mainly to report on the third workshop, held at Bournemouth University in April 2012, but do also consider the lessons learned from all three. We conclude with a roadmap for a fourth (and final) workshop, which is intended to emphasise the overarching importance of the humans using the spac

Vision technology/algorithms for space robotics applications

The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed

Red Sequence Cluster Finding in the Millennium Simulation

We investigate halo mass selection properties of red-sequence cluster finders using galaxy populations of the Millennium Simulation (MS). A clear red sequence exists for MS galaxies in massive halos at redshifts z < 1, and we use this knowledge to inform a cluster-finding algorithm applied to 500 Mpc/h projections of the simulated volume. At low redshift (z=0.4), we find that 90% of the clusters found have galaxy membership dominated by a single, real-space halo, and that 10% are blended systems for which no single halo contributes a majority of a cluster's membership. At z=1, the fraction of blends increases to 22%, as weaker redshift evolution in observed color extends the comoving length probed by a fixed range of color. Other factors contributing to the increased blending at high-z include broadening of the red sequence and confusion from a larger number of intermediate mass halos hosting bright red galaxies of magnitude similar to those in higher mass halos. Our method produces catalogs of cluster candidates whose halo mass selection function, p(M|\Ngal,z), is characterized by a bimodal log-normal model with a dominant component that reproduces well the real-space distribution, and a redshift-dependent tail that is broader and displaced by a factor ~2 lower in mass. We discuss implications for X-ray properties of optically selected clusters and offer ideas for improving both mock catalogs and cluster-finding in future surveys.Comment: final version to appear in MNRAS. Appendix added on purity and completeness, small shift in red sequence due to correcting an error in finding i