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

By Sergey V. Sai, Ilya S. Sai, Nikolay Yu. Sorokin (auth.), Jacques Blanc-Talon, Don Bone, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)

ISBN-10: 3642176879

ISBN-13: 9783642176876

ISBN-10: 3642176887

ISBN-13: 9783642176883

This quantity collects the papers permitted for presentation on the twelfth Int- nationwide convention on “Advanced techniques 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 given that maintained the culture of being a unmarried music convention. ACIVS 2010 attracted machine scientists from 29 di?erent nations, usually from Europe, Australia, and america, but in addition from Asia. even if 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 renowned issues. Noteworthy are the transforming into variety of papers with regards to theoretical devel- ments. we wish 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 collage) for his or her worthwhile 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

Example text

Zhu et al. [4] try to reduce the long search time of 2D Otsu method by proposing a new algorithm that improves the histogram usage. Fengjie et al. [5] 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. [6] 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. Lang et al.

Fengjie et al. [5] 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. [6] 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. Lang et al. [7] propose a fast algorithm to overcome the high computational complexity of the 2D Otsu algorithm. 2 Neural Networks Segmentation Due to the large number of different neural architectures on one hand, and different learning schemes on the other hand, it is difficult to provide an indepth review of all neural approaches to image segmentation.

Considering the phase and magnitude spectra: ϕ(μ, ν) = tan−1 Γ (μ, ν) (6) M (μ, ν) = |Γ (μ, ν)| (7) The distortion in the phase and the magnitude spectra can be calculated and weighted separately. Thus spectral magnitude distortion can be defined as: S= 1 N2 N −1 ˆ (μ, ν)|2 |M (μ, ν) − M (8) μ,ν=0 The spectral phase distortion is: S1 = 1 N2 N −1 |ϕ(μ, ν) − ϕ(μ, ˆ ν)|2 (9) μ,ν=0 The weighted spectral distortion is defined as: N −1 S2 = N −1 1 ˆ (μ, ν)|2 ) (10) (λ |ϕ(μ, ν) − ϕ(μ, ˆ ν)|2 ) + (1 − λ) |M (μ, ν) − M N μ,ν=0 μ,ν=0 where ‘λ’ is chosen to give required weight to the phase and magnitude terms.

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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.)


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