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 thoughts for clever imaginative and prescient platforms” (ACIVS 2010). Following the ?rst assembly in Baden-Baden (Germany) in 1999, whichwaspartofalargemulticonference,theACIVSconferencethendeveloped into an autonomous scienti?c occasion and has ever considering the fact that maintained the culture of being a unmarried tune convention. ACIVS 2010 attracted machine scientists from 29 di?erent international locations, more often than not from Europe, Australia, and the united states, but in addition from Asia. even though ACIVS is a convention on all parts of snapshot and video processing, submissions are likely to assemble inside convinced significant ?elds of curiosity. This yr 3D and intensity processing and laptop imaginative and prescient and surveillance have been well known issues. Noteworthy are the transforming into variety of papers with regards to theoretical devel- ments. we want to thank the invited audio system Mubarak Shah (University of relevant Florida), Richard Kleihorst (VITO, Belgium), Richard Hartley (A- tralian nationwide University), and David Suter (Adelaide college) for his or her worthwhile contributions.
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Additional resources for Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part I
3] made a comparison between Otsu segmentation technique and K-means method. Zhu et al.  try to reduce the long search time of 2D Otsu method by proposing a new algorithm that improves the histogram usage. Fengjie et al.  propose a new algorithm combining the 2D Otsu segmentation technique with genetic algorithms to segment images of cables covered with ice from their background. Zhang et al.  propose a new algorithm that uses 2D histogram from 1D histogram to obtain optimal threshold from the Otsu algorithm by using fast wavelet transform algorithm.
Further, Human Visual System L2 norm (H2) is less responsive to changes in human perception, and therefore does not qualify as a good quality metric for white noise distorted images. Human Visual System Absolute norm (H1) proves to be the best among these measures. References 1. : Why is image quality assessment so diﬃcult. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, pp. 3313–3316 (2002) 2. : Quality measurement for monochrome compressed images in the past 25 years.
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. In: Proc. of IEEE Conf. CVPR 2009, pp. 17–24 (2009) 14. : Random sample consensus: a paradigm for model ﬁtting with applications to image analysis and automated cartography.
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.)