By David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This e-book constitutes the refereed court cases of the thirteenth foreign convention on complicated suggestions for clever imaginative and prescient structures, ACIVS 2011, held in Ghent, Belgium, in August 2011.
The sixty six revised complete papers provided have been rigorously reviewed and chosen from 124 submissions. The papers are geared up in topical sections on class popularity, and monitoring, segmentation, photographs research, photo processing, video surveillance and biometrics, algorithms and optimization; and 3D, intensity and scene understanding.
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
TK such that L < t1 < . . < tK < U (later referred as knots) and K > 2, we consider the space S consisting of the twicecontinuously differentiable function fs on (L, U ), such that the restriction of fs to each of the intervals [t1 , t2 ] . . [tK−1 , tK ] is a cubic polynomial and linear on (L, t1 ] and [tK , U ). The functions of the K-dimensional space S are named natural (cubic) splines. Let 1, B1 , . . , Bp (with p = K−1) a set of basis functions that span the space S, chosen such that B1 is linear with negative slope on (L, t1 ], B2 , .
The density class descriptor (dimension i), this last being estimated from local descriptor extracted from several learning images. The full gradient vector UY (θ) is a concatenation of these partial i derivatives with respect to all parameters. Its number of components is D i=1 p , where i p is the number of non-constant polynomial of the basis of S for dimension i. The equation (14) leads to a remarkably simple expression, for which the physical interpretation is quite straightforward. e density f i ), through a well chosen polynomial basis, at each dimension (figure 1).
In: ICCV, vol. 2, pp. 1470–1477 (2003) 21. : Linear spatial pyramid matching using sparse coding for image classification. In: CVPR (2009) Knowledge-Driven Saliency: Attention to the Unseen M. de Abstract. This paper deals with attention in 3D environments based upon knowledge-driven cues. Using learned 3D scenes as top-down inﬂuence, the proposed system is able to mark high saliency to locations occupied by objects that are new, changed, or even missing from their location as compared to the already learned situation.