396 research outputs found

    Utilising path-vertex data to improve Monte Carlo global illumination.

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    Efficient techniques for photo-realistic rendering are in high demand across a wide array of industries. Notable applications include visual effects for film, entertainment and virtual reality. Less direct applications such as visualisation for architecture, lighting design and product development also rely on the synthesis of realistic and physically based illumination. Such applications assert ever increasing demands on light transport algorithms, requiring the computation of photo-realistic effects while handling complex geometry, light scattering models and illumination. Techniques based on Monte Carlo integration handle such scenarios elegantly and robustly, but despite seeing decades of focused research and wide commercial support, these methods and their derivatives still exhibit undesirable side effects that are yet to be resolved. In this thesis, Monte Carlo path tracing techniques are improved upon by utilizing path vertex data and intermediate radiance contributions readily available during rendering. This permits the development of novel progressive algorithms that render low noise global illumination while striving to maintain the desirable accuracy and convergence properties of unbiased methods. The thesis starts by presenting a discussion into optical phenomenon, physically based rendering and achieving photo realistic image synthesis. This is followed by in-depth discussion of the published theoretical and practical research in this field, with a focus on stochastic methods and modem rendering methodologies. This provides insight into the issues surrounding Monte Carlo integration both in the general and rendering specific contexts, along with an appreciation for the complexities of solving global light transport. Alternative methods that aim to address these issues are discussed, providing an insight into modem rendering paradigms and their characteristics. Thus, an understanding of the key aspects is obtained, that is necessary to build up and discuss the novel research and contributions to the field developed throughout this thesis. First, a path space filtering strategy is proposed that allows the path-based space of light transport to be classified into distinct subsets. This permits the novel combination of robust path tracing and recent progressive photon mapping algorithms to handle each subset based on the characteristics of the light transport in that space. This produces a hybrid progressive rendering technique that utilises the strengths of existing state of the art Monte Carlo and photon mapping methods to provide efficient and consistent rendering of complex scenes with vanishing bias. The second original contribution is a probabilistic image-based filtering and sample clustering framework that provides high quality previews of global illumination whilst remaining aware of high frequency detail and features in geometry, materials and the incident illumination. As will be seen, the challenges of edge-aware noise reduction are numerous and long standing, particularly when identifying high frequency features in noisy illumination signals. Discontinuities such as hard shadows and glossy reflections are commonly overlooked by progressive filtering techniques, however by dividing path space into multiple layers, once again based on utilising path vertex data, the overlapping illumination of varying intensities, colours and frequencies is more effectively handled. Thus noise is removed from each layer independent of features present in the remaining path space, effectively preserving such features

    Activity Analysis; Finding Explanations for Sets of Events

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    Automatic activity recognition is the computational process of analysing visual input and reasoning about detections to understand the performed events. In all but the simplest scenarios, an activity involves multiple interleaved events, some related and others independent. The activity in a car park or at a playground would typically include many events. This research assumes the possible events and any constraints between the events can be defined for the given scene. Analysing the activity should thus recognise a complete and consistent set of events; this is referred to as a global explanation of the activity. By seeking a global explanation that satisfies the activity’s constraints, infeasible interpretations can be avoided, and ambiguous observations may be resolved. An activity’s events and any natural constraints are defined using a grammar formalism. Attribute Multiset Grammars (AMG) are chosen because they allow defining hierarchies, as well as attribute rules and constraints. When used for recognition, detectors are employed to gather a set of detections. Parsing the set of detections by the AMG provides a global explanation. To find the best parse tree given a set of detections, a Bayesian network models the probability distribution over the space of possible parse trees. Heuristic and exhaustive search techniques are proposed to find the maximum a posteriori global explanation. The framework is tested for two activities: the activity in a bicycle rack, and around a building entrance. The first case study involves people locking bicycles onto a bicycle rack and picking them up later. The best global explanation for all detections gathered during the day resolves local ambiguities from occlusion or clutter. Intensive testing on 5 full days proved global analysis achieves higher recognition rates. The second case study tracks people and any objects they are carrying as they enter and exit a building entrance. A complete sequence of the person entering and exiting multiple times is recovered by the global explanation

    Topological data analysis of organoids

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    Organoids are multi-cellular structures which are cultured in vitro from stem cells to resemble specific organs (e.g., colon, liver) in their three- dimensional composition. The gene expression and the tissue composition of organoids constantly affect each other. Dynamic changes in the shape, cellular composition and transcriptomic profile of these model systems can be used to understand the effect of mutations and treatments in health and disease. In this thesis, I propose new techniques in the field of topological data analysis (TDA) to analyse the gene expression and the morphology of organoids. I use TDA methods, which are inspired by topology, to analyse and quantify the continuous structure of single-cell RNA sequencing data, which is embedded in high dimensional space, and the shape of an organoid. For single-cell RNA sequencing data, I developed the multiscale Laplacian score (MLS) and the UMAP diffusion cover, which both extend and im- prove existing topological analysis methods. I demonstrate the utility of these techniques by applying them to a published benchmark single-cell data set and a data set of mouse colon organoids. The methods validate previously identified genes and detect additional genes with known involvement cancers. To study the morphology of organoids I propose DETECT, a rotationally invariant signature of dynamically changing shapes. I demonstrate the efficacy of this method on a data set of segmented videos of mouse small intestine organoid experiments and show that it outperforms classical shape descriptors. I verify the method on a synthetic organoid data set and illustrate how it generalises to 3D to conclude that DETECT offers rigorous quantification of organoids and opens up computationally scalable methods for distinguishing different growth regimes and assessing treatment effects. Finally, I make a theoretical contribution to the statistical inference of the method underlying DETECT

    An algorithmic framework for visualising and exploring multidimensional data

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    To help understand multidimensional data, information visualisation techniques are often applied to take advantage of human visual perception in exposing latent structure. A popular means of presenting such data is via two-dimensional scatterplots where the inter-point proximities reflect some notion of similarity between the entities represented. This can result in potentially interesting structure becoming almost immediately apparent. Traditional algorithms for carrying out this dimension reduction tend to have different strengths and weaknesses in terms of run times and layout quality. However, it has been found that the combination of algorithms can produce hybrid variants that exhibit significantly lower run times while maintaining accurate depictions of high-dimensional structure. The author's initial contribution in the creation of such algorithms led to the design and implementation of a software system (HIVE) for the development and investigation of new hybrid variants and the subsequent analysis of the data they transform. This development was motivated by the fact that there are potentially many hybrid algorithmic combinations to explore and therefore an environment that is conductive to their development, analysis and use is beneficial not only in exploring the data they transform but also in exploring the growing number of visualisation tools that these algorithms beget. This thesis descries three areas of the author's contribution to the field of information visualisation. Firstly, work on hybrid algorithms for dimension reduction is presented and their analysis shows their effectiveness. Secondly, the development of a framework for the creation of tailored hybrid algorithms is illustrated. Thirdly, a system embodying the framework, providing an environment conductive to the development, evaluation and use of the algorithms is described. Case studies are provided to demonstrate how the author and others have used and found value in the system across areas as diverse as environmental science, social science and investigative psychology, where multidimensional data are in abundance

    Towards Fully Dynamic Surface Illumination in Real-Time Rendering using Acceleration Data Structures

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    The improvements in GPU hardware, including hardware-accelerated ray tracing, and the push for fully dynamic realistic-looking video games, has been driving more research in the use of ray tracing in real-time applications. The work described in this thesis covers multiple aspects such as optimisations, adapting existing offline methods to real-time constraints, and adding effects which were hard to simulate without the new hardware, all working towards a fully dynamic surface illumination rendering in real-time.Our first main area of research concerns photon-based techniques, commonly used to render caustics. As many photons can be required for a good coverage of the scene, an efficient approach for detecting which ones contribute to a pixel is essential. We improve that process by adapting and extending an existing acceleration data structure; if performance is paramount, we present an approximation which trades off some quality for a 2–3Γ— improvement in rendering time. The tracing of all the photons, and especially when long paths are needed, had become the highest cost. As most paths do not change from frame to frame, we introduce a validation procedure allowing the reuse of as many as possible, even in the presence of dynamic lights and objects. Previous algorithms for associating pixels and photons do not robustly handle specular materials, so we designed an approach leveraging ray tracing hardware to allow for caustics to be visible in mirrors or behind transparent objects.Our second research focus switches from a light-based perspective to a camera-based one, to improve the picking of light sources when shading: photon-based techniques are wonderful for caustics, but not as efficient for direct lighting estimations. When a scene has thousands of lights, only a handful can be evaluated at any given pixel due to time constraints. Current selection methods in video games are fast but at the cost of introducing bias. By adapting an acceleration data structure from offline rendering that stochastically chooses a light source based on its importance, we provide unbiased direct lighting evaluation at about 30 fps. To support dynamic scenes, we organise it in a two-level system making it possible to only update the parts containing moving lights, and in a more efficient way.We worked on top of the new ray tracing hardware to handle lighting situations that previously proved too challenging, and presented optimisations relevant for future algorithms in that space. These contributions will help in reducing some artistic constraints while designing new virtual scenes for real-time applications

    A lightweight, graph-theoretic model of class-based similarity to support object-oriented code reuse.

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    The work presented in this thesis is principally concerned with the development of a method and set of tools designed to support the identification of class-based similarity in collections of object-oriented code. Attention is focused on enhancing the potential for software reuse in situations where a reuse process is either absent or informal, and the characteristics of the organisation are unsuitable, or resources unavailable, to promote and sustain a systematic approach to reuse. The approach builds on the definition of a formal, attributed, relational model that captures the inherent structure of class-based, object-oriented code. Based on code-level analysis, it relies solely on the structural characteristics of the code and the peculiarly object-oriented features of the class as an organising principle: classes, those entities comprising a class, and the intra and inter-class relationships existing between them, are significant factors in defining a two-phase similarity measure as a basis for the comparison process. Established graph-theoretic techniques are adapted and applied via this model to the problem of determining similarity between classes. This thesis illustrates a successful transfer of techniques from the domains of molecular chemistry and computer vision. Both domains provide an existing template for the analysis and comparison of structures as graphs. The inspiration for representing classes as attributed relational graphs, and the application of graph-theoretic techniques and algorithms to their comparison, arose out of a well-founded intuition that a common basis in graph-theory was sufficient to enable a reasonable transfer of these techniques to the problem of determining similarity in object-oriented code. The practical application of this work relates to the identification and indexing of instances of recurring, class-based, common structure present in established and evolving collections of object-oriented code. A classification so generated additionally provides a framework for class-based matching over an existing code-base, both from the perspective of newly introduced classes, and search "templates" provided by those incomplete, iteratively constructed and refined classes associated with current and on-going development. The tools and techniques developed here provide support for enabling and improving shared awareness of reuse opportunity, based on analysing structural similarity in past and ongoing development, tools and techniques that can in turn be seen as part of a process of domain analysis, capable of stimulating the evolution of a systematic reuse ethic

    Strategies for image visualisation and browsing

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    PhDThe exploration of large information spaces has remained a challenging task even though the proliferation of database management systems and the state-of-the art retrieval algorithms is becoming pervasive. Signi cant research attention in the multimedia domain is focused on nding automatic algorithms for organising digital image collections into meaningful structures and providing high-semantic image indices. On the other hand, utilisation of graphical and interactive methods from information visualisation domain, provide promising direction for creating e cient user-oriented systems for image management. Methods such as exploratory browsing and query, as well as intuitive visual overviews of image collection, can assist the users in nding patterns and developing the understanding of structures and content in complex image data-sets. The focus of the thesis is combining the features of automatic data processing algorithms with information visualisation. The rst part of this thesis focuses on the layout method for displaying the collection of images indexed by low-level visual descriptors. The proposed solution generates graphical overview of the data-set as a combination of similarity based visualisation and random layout approach. Second part of the thesis deals with problem of visualisation and exploration for hierarchical organisation of images. Due to the absence of the semantic information, images are considered the only source of high-level information. The content preview and display of hierarchical structure are combined in order to support image retrieval. In addition to this, novel exploration and navigation methods are proposed to enable the user to nd the way through database structure and retrieve the content. On the other hand, semantic information is available in cases where automatic or semi-automatic image classi ers are employed. The automatic annotation of image items provides what is referred to as higher-level information. This type of information is a cornerstone of multi-concept visualisation framework which is developed as a third part of this thesis. This solution enables dynamic generation of user-queries by combining semantic concepts, supported by content overview and information ltering. Comparative analysis and user tests, performed for the evaluation of the proposed solutions, focus on the ways information visualisation a ects the image content exploration and retrieval; how e cient and comfortable are the users when using di erent interaction methods and the ways users seek for information through di erent types of database organisation

    Hyperbolic Deep Neural Networks: A Survey

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    Recently, there has been a rising surge of momentum for deep representation learning in hyperbolic spaces due to theirhigh capacity of modeling data like knowledge graphs or synonym hierarchies, possessing hierarchical structure. We refer to the model as hyperbolic deep neural network in this paper. Such a hyperbolic neural architecture potentially leads to drastically compact model withmuch more physical interpretability than its counterpart in Euclidean space. To stimulate future research, this paper presents acoherent and comprehensive review of the literature around the neural components in the construction of hyperbolic deep neuralnetworks, as well as the generalization of the leading deep approaches to the Hyperbolic space. It also presents current applicationsaround various machine learning tasks on several publicly available datasets, together with insightful observations and identifying openquestions and promising future directions
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