Download e-book for iPad: 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.)

ISBN-10: 3642236863

ISBN-13: 9783642236860

ISBN-10: 3642236871

ISBN-13: 9783642236877

This ebook constitutes the refereed lawsuits of the thirteenth overseas convention on complicated innovations for clever imaginative and prescient platforms, ACIVS 2011, held in Ghent, Belgium, in August 2011.

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

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Read Online or Download Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings PDF

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

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However, 30 H. Le Borgne and P. Mu˜noz Fuentes Table 1. Performances on the vcdt08 benchmark for several implementations of our method (see text). The lower the EER, the better the method. 254 Table 2. Classification accuracy on scene15 for three different implementation (see text). The equation number is given for the centered and the normalized signature. The simple one is only the first part of equation (14). Signatures were computed with 32 knots and no grid nor pyramid. 42 when the signature is computed with the “above knots” implementation, the binarized weighting scheme allows to reach similar performances as those obtained with the nonredundant implementation.

2(c) and (d), from which we can easily identify classes A and B. Obviously, the supervised vocabulary is more discriminative to classify A and B. Please note that the category information is only used at training stage to obtain the vocabulary, and at testing stage, each feature is assigned to its nearest visual words (as Fig. 1(b) shows) using the traditional distance measurement without using category information. However, we can expect that the visual vocabulary obtained by the supervised method has high class purity for nonannotated testing dataset if it has similar feature distribution with the training dataset.

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.

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Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings by David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)


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