13,056 research outputs found
J. von Braun, A. Gulati, S. Fan, M.S. Ahluawalia etj. Liu, Lessons learned front China and India
No full textÉtienne Gilbert. J. von Braun, A. Gulati, S. Fan, M.S. Ahluawalia etj. Liu, Lessons learned front China and India. In: Tiers-Monde, tome 47, n°186, 2006. Asie : les enjeux d'une croissance élevée, sous la direction de Sunanda Sen . p. 460
J. von Braun, A. Gulati, S. Fan, M.S. Ahluawalia etj. Liu, Lessons learned front China and India
No full textÉtienne Gilbert. J. von Braun, A. Gulati, S. Fan, M.S. Ahluawalia etj. Liu, Lessons learned front China and India. In: Tiers-Monde, tome 47, n°186, 2006. Asie : les enjeux d'une croissance élevée, sous la direction de Sunanda Sen . p. 460
Morphometric and meristic studies on the yellow sea bream (Dentex tumifrons) from the South and the East China Seas
No full text
On using Directional Information for Parameter Space Decomposition in Ellipse Detection
No full textIn this paper we use the parametric polar representation to extend the application of edge directional information from circle to ellipse extraction. As a result we obtain a mapping which decomposes the parameter space required for ellipse extraction into two independent sub-spaces and one final histogram accumulator. The mapping includes the tangent of the angle of the first and second directional derivatives. These tangents are computed by considering edge direction at two border points. We show that the use of gradient information for parameter space decomposition avoids the intensive point labelling imposed by geometric constraints used by other approaches
Inhibition of cell-induced vitreous contraction by synthetic peptide derived from the collagen receptor binding sequence.
No full textSuper resolution using edge prior and single image detail synthesis
No full textEdge-directed image super resolution (SR) focuses on ways to remove edge artifacts in upsampled images. Under large magnification, however, textured regions become blurred and appear homogenous, resulting in a super-resolution image that looks unnatural. Alternatively, learning-based SR approaches use a large database of exemplar images for “hallucinating” detail. The quality of the upsampled image, especially about edges, is dependent on the suitability of the training images. This paper aims to combine the benefits of edge-directed SR with those of learning-based SR. In particular, we propose an approach to extend edge-directed super-resolution to include detail from an image/texture example provided by the user (e.g., from the Internet). A significant benefit of our approach is that only a single exemplar image is required to supply the missing detail - strong edges are obtained in the SR image even if they are not present in the example image due to the combination of the edge-directed approach. In addition, we can achieve quality results at very large magnification, which is often problematic for both edge-directed and learning-based approaches
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