Download Advanced Concepts for Intelligent Vision Systems: 12th by Sergey V. Sai, Ilya S. Sai, Nikolay Yu. Sorokin (auth.), PDF

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 authorised for presentation on the twelfth Int- nationwide convention on “Advanced techniques for clever imaginative and prescient structures” (ACIVS 2010). Following the ?rst assembly in Baden-Baden (Germany) in 1999, whichwaspartofalargemulticonference,theACIVSconferencethendeveloped into an self sustaining scienti?c occasion and has ever on account that maintained the culture of being a unmarried song convention. ACIVS 2010 attracted computing device scientists from 29 di?erent nations, commonly from Europe, Australia, and the united states, but additionally from Asia. even if ACIVS is a convention on all components of photograph and video processing, submissions are likely to assemble inside yes significant ?elds of curiosity. This 12 months 3D and intensity processing and desktop imaginative and prescient and surveillance have been well known issues. Noteworthy are the turning out to be variety of papers relating to theoretical devel- ments. we want to thank the invited audio system Mubarak Shah (University of significant 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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Additional info for Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part I

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18–29, 2010. c Springer-Verlag Berlin Heidelberg 2010 Real-Time Retrieval of Near-Duplicate Fragments 19 images contain any visually similar (near-duplicate) fragments without any prior knowledge on how many such fragments (if any) exist. The problem can be specified as follows: Given two random images I and J , identify in them pairs of near-duplicate image fragments. The term ”near-duplicate fragments” refers to unspecified fragments of (almost) identical objects. However, the visual appearances of those objects may differ because of different scene and camera settings, photometric conditions, digitization parameters and possibly because of certain natural deformations.

A low-dimensional local descriptor incorporating tps warping for image matching. Image and Vision Computing 28(8), 1184–1195 (2010) 11. : Scale-rotation invariant pattern entropy for keypointbased near-duplicate detection. IEEE Transactions on Image Processing 18, 412– 423 (2009) 12. : Key-places detection and clustering in movies using latent aspects. In: Proc. 14th IEEE Int. Conf. Image Processing, vol. 2, pp. 228 (2007) Real-Time Retrieval of Near-Duplicate Fragments 29 13. : Geometric min-hashing: Finding a (thick) needle in a haystack.

4 shows the urban infrastructure shadows superimposed to its corresponding intensity map. In the Table 1, we present some interesting measures about the performance of the system, specially related to Fig. 4. Note how, despite apparent low true positive and high false negative rates, TRP and FPR respectively, the algorithm gives good visual results. In particular, it has been observed that high frequency detail is easily missed during smoothing, previous to edge detection, taking out a large amounts of urban infrastructure edge shadows.

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