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Download Energy Minimization Methods in Computer Vision and Pattern by Haithem Boussaid, Iasonas Kokkinos, Nikos Paragios (auth.), PDF

By Haithem Boussaid, Iasonas Kokkinos, Nikos Paragios (auth.), Anders Heyden, Fredrik Kahl, Carl Olsson, Magnus Oskarsson, Xue-Cheng Tai (eds.)

This quantity constitutes the refereed court cases of the ninth foreign convention on strength Minimization equipment in computing device imaginative and prescient and trend attractiveness, EMMCVPR 2013, held in Lund, Sweden, in August 2013. The 26 revised complete papers have been conscientiously reviewed and chosen from forty submissions. The papers are geared up in topical sections on scientific Imaging; picture enhancing; 3D Reconstruction; form Matching; Scene knowing; Segmentation; Superpixels; Statistical tools and Learning.

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Read or Download Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th International Conference, EMMCVPR 2013, Lund, Sweden, August 19-21, 2013. Proceedings PDF

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Additional info for Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th International Conference, EMMCVPR 2013, Lund, Sweden, August 19-21, 2013. Proceedings

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As a remedy, one may want to rescale the results such that the values in the non-shadow region approximate the ones in the initial image. This is done in Figure 3(d). 3 Seamless Image Cloning The property of osmosis to fuse incompatible information can also be used for seamless image cloning. Figure 4 illustrates the problem: Two images f1 and f2 are to be merged such that f2 replaces image information of f1 . The rectangular image domain of the original image f1 is denoted by Ω, and the image domain that is to be inserted is Γ .

In Section 2 we describe our drift-diffusion framework for continuous osmosis filters, analyse its essential properties, and interprete osmosis processes as models for data integration. Afterwards we sketch a simple numerical scheme in Section 3. Applications of osmosis models to visual computing problems are described in Section 4, and our paper is concluded with a summary in Section 5. Related Work. While diffusion filters are often combined with data fidelity terms, there are not many combinations with a drift term in divergence form so far.

This scheme also holds for boundary points, if we mirror the image at its boundaries and assume a zero drift vector across boundaries. For some positive image v, we obtain a discrete approximation of its canonical drift vector field (d1 [v], d2 [v]) = ∇v v at intermediate grid points via d1,i+ 12 ,j = 2 (vi+1,j − vi,j ) , h (vi+1,j + vi,j ) d2,i,j+ 12 = 2 (vi,j+1 − vi,j ) . h (vi,j+1 + vi,j ) (21) Linear Osmosis Models for Visual Computing 33 In [15] we show that the scheme (20)–(21) preserves positivity and converges to its unique steady state if the time step size satisfies τ < 4 h2 .

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