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

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

This booklet constitutes the refereed court cases of the thirteenth overseas convention on complex suggestions for clever imaginative and prescient platforms, ACIVS 2011, held in Ghent, Belgium, in August 2011.

The sixty six revised complete papers awarded have been rigorously reviewed and chosen from 124 submissions. The papers are prepared in topical sections on class acceptance, and monitoring, segmentation, pictures research, snapshot processing, video surveillance and biometrics, algorithms and optimization; and 3D, intensity and scene understanding.

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Additional resources for Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings

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Hence we report the higher accuracy on this state-of-the-art dataset in scene classification. We also reported competitive results on the vcdt08 benchmark of the ImageCLEF campaign [3]. In future work we would be interested in applying the non-parametric Fisher vector estimation presented in this paper on local features that only partially verify the property of statistical independence (see [11]) or not at all [14]. Another direction of research will concern the signature compression in order to apply it to image retrieval.

2. Hence, it exists a basis 1, B1i , . . , Bpi i of S such that: ⎛ i ⎞ h (x , θ ) = exp ⎝ i i p θji Bji (xi ) − C i (θi ))⎠ i (9) j=1 Let {yt , t = 1 . . T } a set of vectors extracted from a given image, seen as T independent realizations of the D-dimensional random vector Y (for simplicity, Y denotes both the image and the corresponding random vector). The log-likelihood is thus: T L(Y, θ) = log (h(yt , θ)) (10) t=1 Where h(yt , θ) denotes the density of Y . 6), we have: D hi (yti , θi ) h(yt , θ) = (11) i=1 Thus: T D L(Y, θ) = log hi (yti , θi ) (12) t=1 i=1 Each density hi can be estimated on the same basis as the one determined during learning.

The performance is measured using the classification rate to be comparable to previous works. The second dataset (vcdt08) was used as a benchmark in the Visual Concept Detection Task of the campaign ImageCLEF 2008 [3]. e an image can belong to several of these categories and sometimes to none of them. e the point such that the false acceptance rate is equal to the false rejection rate. Hence, this value is between 0 and 1 such that the smaller the EER the better the system: ideally, false acceptance and false rejection are null.

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