Advanced Concepts for Intelligent Vision Systems: 13th by David Van Hamme, Peter Veelaert, Wilfried Philips (auth.),

By David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)

This publication constitutes the refereed complaints of the thirteenth foreign convention on complicated ideas for clever imaginative and prescient platforms, ACIVS 2011, held in Ghent, Belgium, in August 2011.

The sixty six revised complete papers provided have been conscientiously reviewed and chosen from 124 submissions. The papers are geared up in topical sections on type attractiveness, and monitoring, segmentation, pictures research, snapshot processing, video surveillance and biometrics, algorithms and optimization; and 3D, intensity and scene understanding.

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6, the knots are approximately placed according to a logarithmic distribution. Once the knots (t1 , . . , tK ) are fixed, a natural choice for the set of basis functions (1, B1 , . . ) leads to compute the difference of the mean between the considered image (density hi ) and the class (density f i ). ) are the positive part of a cubic function that is null at knot tk . Obviously, such a representation is strongly redundant. Hence, we propose two simplifications to this implementation. First, for polynomials Bk with k > 1, we only 28 H.

It finally results into the estimation of the maximum likelihood estimate ˆθ of the coefficients, the knots t1 , . . , tK and thus the wanted density. Let notice that at convergence (θ = ˆ θ), the loglikelihood is maximal, thus its derivative is null. Let {y1 . . yn } a random sample from f . ) according to f . This will be used later in our development. 3 Signature Derivation Let consider that any image is described according to some D-dimensional vectors. Each feature dimension xi can be thought of as arising as a random sample from a distribution having a density hi .

The proposed method runs like the k-means algorithm to minimize the clustering cost function. The experiments on different datasets demonstrate that the proposed vocabulary construction method is effective for the application of image classification. Acknowledgements. This work was partially funded by Mediapro through the Spanish project CENIT-2007-1012 i3media and by the Centro para el Desarrollo Tecnol´ogico Industrial (CDTI). The authors acknowlege partial support by the EU project “2020 3D Media: Spatial Sound and Vision” under FP7-ICT.

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