819 research outputs found

    Side information in robust principal component analysis: algorithms and applications

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    Dimensionality reduction and noise removal are fundamental machine learning tasks that are vital to artificial intelligence applications. Principal component analysis has long been utilised in computer vision to achieve the above mentioned goals. Recently, it has been enhanced in terms of robustness to outliers in robust principal component analysis. Both convex and non-convex programs have been developed to solve this new formulation, some with exact convergence guarantees. Its effectiveness can be witnessed in image and video applications ranging from image denoising and alignment to background separation and face recognition. However, robust principal component analysis is by no means perfect. This dissertation identifies its limitations, explores various promising options for improvement and validates the proposed algorithms on both synthetic and real-world datasets. Common algorithms approximate the NP-hard formulation of robust principal component analysis with convex envelopes. Though under certain assumptions exact recovery can be guaranteed, the relaxation margin is too big to be squandered. In this work, we propose to apply gradient descent on the Burer-Monteiro bilinear matrix factorisation to squeeze this margin given available subspaces. This non-convex approach improves upon conventional convex approaches both in terms of accuracy and speed. On the other hand, oftentimes there is accompanying side information when an observation is made. The ability to assimilate such auxiliary sources of data can ameliorate the recovery process. In this work, we investigate in-depth such possibilities for incorporating side information in restoring the true underlining low-rank component from gross sparse noise. Lastly, tensors, also known as multi-dimensional arrays, represent real-world data more naturally than matrices. It is thus advantageous to adapt robust principal component analysis to tensors. Since there is no exact equivalence between tensor rank and matrix rank, we employ the notions of Tucker rank and CP rank as our optimisation objectives. Overall, this dissertation carefully defines the problems when facing real-world computer vision challenges, extensively and impartially evaluates the state-of-the-art approaches, proposes novel solutions and provides sufficient validations on both simulated data and popular real-world datasets for various mainstream computer vision tasks.Open Acces

    Enhancing Mesh Deformation Realism: Dynamic Mesostructure Detailing and Procedural Microstructure Synthesis

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    Propomos uma solução para gerar dados de mapas de relevo dinâmicos para simular deformações em superfícies macias, com foco na pele humana. A solução incorpora a simulação de rugas ao nível mesoestrutural e utiliza texturas procedurais para adicionar detalhes de microestrutura estáticos. Oferece flexibilidade além da pele humana, permitindo a geração de padrões que imitam deformações em outros materiais macios, como couro, durante a animação. As soluções existentes para simular rugas e pistas de deformação frequentemente dependem de hardware especializado, que é dispendioso e de difícil acesso. Além disso, depender exclusivamente de dados capturados limita a direção artística e dificulta a adaptação a mudanças. Em contraste, a solução proposta permite a síntese dinâmica de texturas que se adaptam às deformações subjacentes da malha de forma fisicamente plausível. Vários métodos foram explorados para sintetizar rugas diretamente na geometria, mas sofrem de limitações como auto-interseções e maiores requisitos de armazenamento. A intervenção manual de artistas na criação de mapas de rugas e mapas de tensão permite controle, mas pode ser limitada em deformações complexas ou onde maior realismo seja necessário. O nosso trabalho destaca o potencial dos métodos procedimentais para aprimorar a geração de padrões de deformação dinâmica, incluindo rugas, com maior controle criativo e sem depender de dados capturados. A incorporação de padrões procedimentais estáticos melhora o realismo, e a abordagem pode ser estendida além da pele para outros materiais macios.We propose a solution for generating dynamic heightmap data to simulate deformations for soft surfaces, with a focus on human skin. The solution incorporates mesostructure-level wrinkles and utilizes procedural textures to add static microstructure details. It offers flexibility beyond human skin, enabling the generation of patterns mimicking deformations in other soft materials, such as leater, during animation. Existing solutions for simulating wrinkles and deformation cues often rely on specialized hardware, which is costly and not easily accessible. Moreover, relying solely on captured data limits artistic direction and hinders adaptability to changes. In contrast, our proposed solution provides dynamic texture synthesis that adapts to underlying mesh deformations. Various methods have been explored to synthesize wrinkles directly to the geometry, but they suffer from limitations such as self-intersections and increased storage requirements. Manual intervention by artists using wrinkle maps and tension maps provides control but may be limited to the physics-based simulations. Our research presents the potential of procedural methods to enhance the generation of dynamic deformation patterns, including wrinkles, with greater creative control and without reliance on captured data. Incorporating static procedural patterns improves realism, and the approach can be extended to other soft-materials beyond skin

    {3D} Morphable Face Models -- Past, Present and Future

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    In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications

    Tensor Regression

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    Regression analysis is a key area of interest in the field of data analysis and machine learning which is devoted to exploring the dependencies between variables, often using vectors. The emergence of high dimensional data in technologies such as neuroimaging, computer vision, climatology and social networks, has brought challenges to traditional data representation methods. Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data. In this book, the authors provide a systematic study and analysis of tensor-based regression models and their applications in recent years. It groups and illustrates the existing tensor-based regression methods and covers the basics, core ideas, and theoretical characteristics of most tensor-based regression methods. In addition, readers can learn how to use existing tensor-based regression methods to solve specific regression tasks with multiway data, what datasets can be selected, and what software packages are available to start related work as soon as possible. Tensor Regression is the first thorough overview of the fundamentals, motivations, popular algorithms, strategies for efficient implementation, related applications, available datasets, and software resources for tensor-based regression analysis. It is essential reading for all students, researchers and practitioners of working on high dimensional data.Comment: 187 pages, 32 figures, 10 table

    Change blindness: eradication of gestalt strategies

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    Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

    Landmark Localization, Feature Matching and Biomarker Discovery from Magnetic Resonance Images

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    The work presented in this thesis proposes several methods that can be roughly divided into three different categories: I) landmark localization in medical images, II) feature matching for image registration, and III) biomarker discovery in neuroimaging. The first part deals with the identification of anatomical landmarks. The motivation stems from the fact that the manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. In this thesis we present three methods to tackle this challenge: A landmark descriptor based on local self-similarities (SS), a subspace building framework based on manifold learning and a sparse coding landmark descriptor based on data-specific learned dictionary basis. The second part of this thesis deals with finding matching features between a pair of images. These matches can be used to perform a registration between them. Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration can be challenging to achieve using intensity based registration algorithms. Here, a framework is proposed for learning correspondences in pairs of images by matching SS features and random sample and consensus (RANSAC) is employed as a robust model estimator to learn a deformation model based on feature matches. Finally, the third part of the thesis deals with biomarker discovery using machine learning. In this section a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability is proposed. The manifold subspace is built using data-driven regions of interest (ROI). These regions are learned via sparse regression, with stability selection. Also, probabilistic distribution models for different stages in the disease trajectory are estimated for different class populations in the low-dimensional manifold and used to construct a probabilistic scoring function.Open Acces

    Proceedings, MSVSCC 2018

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    Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp
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