Titlebook: Computer Vision – ECCV 2012; 12th European Confer Andrew Fitzgibbon,Svetlana Lazebnik,Cordelia Schmi Conference proceedings 2012 Springer-V

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The Secret of Protein Sophistication,-similarity structures, nonaccidental alignment, and instance-specific modelling. The method searches for self-similar image structures that form nonaccidental patterns, for example collinear arrangements. We demonstrate a simple implementation of this idea where self-similar structures are found by

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Sergei Kurgalin,Sergei Borzunovnatural three dimensional environments, it is important to study whether and how depth information influences visual saliency. In this work, we first collect a large human eye fixation database compiled from a pool of 600 2D-vs-3D image pairs viewed by 80 subjects, where the depth information is dir

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Sergei Kurgalin,Sergei Borzunovin images (see, e.g., [1–3]). We systematically integrate and evaluate quaternion DCT- and FFT-based spectral saliency detection [3,4], weighted quaternion color space components [5], and the use of multiple resolutions [1]. Furthermore, we propose the use of the eigenaxes and eigenangles for spectr

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The Disentanglement of Populationse well-known Integral Histogram (IH), our approach significantly outperforms it, both in terms of memory requirements and of response times. By preprocessing the region of interest (ROI) computing and storing a temporary histogram for each of its pixels, IH is effective only when a large amount of h

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https://doi.org/10.1057/9780230297685paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of the recently introduced Stein kernel (related to a symmetric version of Bregman matrix divergence), we propose to perform sparse