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 authorized for presentation on the twelfth Int- nationwide convention on “Advanced suggestions 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 because maintained the culture of being a unmarried music convention. ACIVS 2010 attracted laptop scientists from 29 di?erent international locations, often from Europe, Australia, and america, but in addition from Asia. even though ACIVS is a convention on all components of photo and video processing, submissions are inclined to assemble inside yes significant ?elds of curiosity. This yr 3D and intensity processing and machine imaginative and prescient and surveillance have been renowned issues. Noteworthy are the turning out to be variety of papers with regards to theoretical devel- ments. we want to thank the invited audio system Mubarak Shah (University of imperative Florida), Richard Kleihorst (VITO, Belgium), Richard Hartley (A- tralian nationwide University), and David Suter (Adelaide college) for his or her important contributions.
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Extra info for Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part I
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.
This shows that variation in H1 does not depend on image contents but on the noise level. 100 80 HPV 60 40 20 0 0 150 300 S2 450 600 750 Fig. 2. Human Perception Value versus Spectral Phase-Magnitude Error Subjective Evaluation of Image Quality Measures 15 100 80 HPV 60 40 20 0 0 20 S540 60 80 Fig. 3. 8 Fig. 4. Human Perception Value versus Human Visual System Absolute Norm – In Figure 5, we observe that for a small variation in H2 there is a large variation in human perception values till value 30 on vertical axis, indicating H2 is less responsive to white noise in this range.
The software has been tested using a variety of low- and medium-class webcams. The capturing resolutions is 640 × 480 (the recommended option) or 320 × 240. In the latter case, the quality of images is very poor and matching is far from perfect, but nevertheless still possible. 28 4 ´ A. Sluzek and M. Paradowski Conclusions In the paper we have outlined a topology-based approach to image fragment matching in real time. In earlier papers, , we have suggested applications of the proposed method in assisting visually handicapped people and autonomous agents navigating in unknown and unexplored environments.