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

沙漠

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

esoteric

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