Read e-book online Scale Space and Variational Methods in Computer Vision: PDF

By Yossi Ferdman, Chen Sagiv, Nir Sochen (auth.), Fiorella Sgallari, Almerico Murli, Nikos Paragios (eds.)

ISBN-10: 3540728228

ISBN-13: 9783540728221

ISBN-10: 3540728236

ISBN-13: 9783540728238

This ebook constitutes the refereed lawsuits of the 1st overseas convention on Scale house equipment and Variational tools in laptop imaginative and prescient, SSVM 2007, emanated from the joint variation of the 4th overseas Workshop on Variational, Geometric and point Set equipment in machine imaginative and prescient, VLSM 2007 and the sixth overseas convention on Scale area and PDE equipment in laptop imaginative and prescient, Scale-Space 2007, held in Ischia Italy, May/June 2007.

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Read or Download Scale Space and Variational Methods in Computer Vision: First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings PDF

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Extra info for Scale Space and Variational Methods in Computer Vision: First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings

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The regularized scale space image Iσ at scale σ associated to the initial image I on Rd minimizes the functional E defined as ∞ E[Υ] := (Υ(x) − I(x))2 + 1 2 i=1 Rd σ2i ∂|N| Υ(x) 2N! ∂xN |N|=i 2 dx , (5) where N = N1 , . . , Nd (6) is a multi-index used to denote derivatives of order d |N| = Ni (7) i=1 and N! , d Ni ! , N! = i=1 (8) 28 M. Loog where d is the dimensionality of the images in the space. The first term on the right hand side penalizes deviations of the function Υ from the given image I, while the second part is the regularization term for Υ, not involving I.

3 Marginalization of the Scale Space Metric Given that none of the components in the prior model governing scale space are correlated, if one is interested in a metric only involving a subset of the components, it may be reasonable to simply restricting the distance calculation to this subset in order The Jet Metric 29 to provide a metric in this lower-dimensional space. A similar situation arises in threedimensional Euclidean space, when one is merely interested in the first two dimensions. In that case, the distance between points would be given by considering their distance in the two-dimensional Euclidean space corresponding to the first two coordinates, the third dimension is simply discarded in the calculation of the metric.

Sparse representation for coarse and fine object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006) 555–567 8. : Retrieving images by appearance. In: Proceedings of the 6th International Conference on Computer Vision. (1998) 608–613 9. : Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 530–535 10. : A differential geometric approach to anisotropic diffusion. In: Geometry-Driven Diffusion in Computer Vision.

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Scale Space and Variational Methods in Computer Vision: First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings by Yossi Ferdman, Chen Sagiv, Nir Sochen (auth.), Fiorella Sgallari, Almerico Murli, Nikos Paragios (eds.)


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