By Sergey V. Sai, Ilya S. Sai, Nikolay Yu. Sorokin (auth.), Jacques Blanc-Talon, Don Bone, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This quantity collects the papers approved for presentation on the twelfth Int- nationwide convention on “Advanced suggestions for clever imaginative and prescient platforms” (ACIVS 2010). Following the ?rst assembly in Baden-Baden (Germany) in 1999, whichwaspartofalargemulticonference,theACIVSconferencethendeveloped into an self reliant scienti?c occasion and has ever due to the fact that maintained the culture of being a unmarried music convention. ACIVS 2010 attracted desktop scientists from 29 di?erent nations, quite often from Europe, Australia, and america, but additionally from Asia. even supposing ACIVS is a convention on all parts of picture and video processing, submissions are likely to assemble inside yes significant ?elds of curiosity. This 12 months 3D and intensity processing and laptop imaginative and prescient and surveillance have been well known issues. Noteworthy are the turning out to be variety of papers concerning theoretical devel- ments. we wish to thank the invited audio system Mubarak Shah (University of critical Florida), Richard Kleihorst (VITO, Belgium), Richard Hartley (A- tralian nationwide University), and David Suter (Adelaide collage) for his or her helpful contributions.
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Extra resources for Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part I
Paradowski manually outlined (see Fig. 2) the convex hull of all keypoints detected within the outlines is used as the ground-truth shape of the underlying fragment. Similarly, the convex hull of keypoints clusted by the algorithm is considered the extracted shape of a fragment. Then, we can measure how accurately (in terms of the overlapping areas) the shapes of matches near-duplicates have been estimated (more details in ). e. precision, recall and fmeasure in the object aspect, and the same three measures in the area aspect.
These methods have high precision (both in terms of the area and object aspects) but their recall is lower. They can ﬁnd planar (or nearly planar) surfaces, but they are not designed to handle deformable or highly non-planar objects. 4). These interiors are large sections of the actual similar objects, but some parts remain undetected so that recall is lower (in particular in the area aspect). The topological method has been designed to more robustly handle object deformations. Thus, we expect higher recall values.
Scale and aﬃne invariant interest point detectors. Int. J. Comput. Vision 60, 63–86 (2004) 20. : Robust wide-baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384–393 (2002) 21. : A performance evaluation of local descriptors. IEEE Trans. PAMI 27, 1615–1630 (2005) 22. : Speeded-up robust features (surf). Computer Vision and Image Understanding 110, 346–359 (2008) 23. : Fast scale invariant feature detection and matching on programmable graphics hardware.
Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part I by Sergey V. Sai, Ilya S. Sai, Nikolay Yu. Sorokin (auth.), Jacques Blanc-Talon, Don Bone, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)