Artificial Intelligence Applications and Innovations: 11th by Richard Chbeir, Yannis Manolopoulos, Ilias Maglogiannis,

By Richard Chbeir, Yannis Manolopoulos, Ilias Maglogiannis, Reda Alhajj

This booklet constitutes the refereed complaints of the eleventh IFIP WG 12.5 foreign convention on man made Intelligence functions and concepts, AIAI 2015, held in Bayonne, France, in September 2015. The 35 revised complete papers offered have been rigorously reviewed and chosen from eighty one submissions. The papers characteristic hugely novel study describing cutting edge algorithms, platforms, instruments and strategies, in addition to offering complicated prototypes within the zone of man-made intelligence.

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By Richard Chbeir, Yannis Manolopoulos, Ilias Maglogiannis, Reda Alhajj

This booklet constitutes the refereed complaints of the eleventh IFIP WG 12.5 foreign convention on man made Intelligence functions and concepts, AIAI 2015, held in Bayonne, France, in September 2015. The 35 revised complete papers offered have been rigorously reviewed and chosen from eighty one submissions. The papers characteristic hugely novel study describing cutting edge algorithms, platforms, instruments and strategies, in addition to offering complicated prototypes within the zone of man-made intelligence.

Show description

Read or Download Artificial Intelligence Applications and Innovations: 11th IFIP WG 12.5 International Conference, AIAI 2015, Bayonne, France, September 14–17, 2015, Proceedings PDF

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Additional info for Artificial Intelligence Applications and Innovations: 11th IFIP WG 12.5 International Conference, AIAI 2015, Bayonne, France, September 14–17, 2015, Proceedings

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Using a probabilistic formulation for the image registration problem [7], the uncertainty of a transformation at point can be modeled by the following equation: ( ( )|( ( ), )) = 1 − ( ( ), (( ) ( ) ( ), ( ( )) )) (1) We model the likelihood term (( ( ), )| ( )) as a normal distribution of the intensity difference between transformed and estimated. Comparably, the prior of the transformation, ( ( )), is modeled as a Rician distribution of the Jacobian determinant of the transformation [22]. The distribution is estimated based on the inversion technique proposed in [23].

In general, the introduction of a non-existing pose (j) is not crucial since it can be overcome by neglecting it if too few (numj) optic hulls ( Vi ) have been assigned to it. However, merging a pose with a non-corresponding hull produces a wrong pose, which cannot be recovered a-posteriori. 1 Calculation of Reconstruction Error The use of synthetic data allows the accurate quantification of the error in volume reconstruction. As described above, the optic hull is generated in the form of a volumetric model V.

ACM Transactions on Internet Technology (TOIT) 12(4) (2013) 10. : On certain integrals of Lipschitz-Hankel type involving products of Bessel functions. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 247(935), 529–551 (1955) 11. : Content-based image retrieval using color difference histogram. Pattern Recognition 46(1), 188–198 (2013) 12. : Content-based image retrieval with lIRe and SURF on a smartphone-based product image database. Y.

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