By David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This e-book constitutes the refereed court cases of the thirteenth foreign convention on complex strategies for clever imaginative and prescient structures, ACIVS 2011, held in Ghent, Belgium, in August 2011.
The sixty six revised complete papers offered have been rigorously reviewed and chosen from 124 submissions. The papers are prepared in topical sections on class reputation, and monitoring, segmentation, pictures research, photo processing, video surveillance and biometrics, algorithms and optimization; and 3D, intensity and scene understanding.
Read or Download Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings PDF
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Extra info for Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings
Vcz , vc ) = min (Δ(vcx , vc ), Δ(vcy , vc ))} It may be noted that without the above mentioned step-wise reﬁnements a vc ∈ Oi could try to align itself any visually similar va ∈ Oj leading to high probability of merging errors. Ideally all members of T (Oi , Oj ) should have the same value of transformation but practically it may not be the case. ) counts elements in the given set having similar values to the ﬁrst parameter. Mean of the elements collected in CT is taken as the transformation magnitude to align Oi to Oj .
Handling representation and recognition of 3D objects has been a topic of research for some time. One of the recent proposals includes  with a learning approach that ﬁnds salient points on a 3D object and represents these points in a 2D spatial map based on a longitude-latitude transformation. Results of classiﬁcation have been demonstrated on isolated objects. Use of canonical views has 36 M. Zaheer Aziz, M. Knopf, and B. Mertsching been experimented in  where isolated objects are converted into representations having predeﬁned size.
WD (size Δ) that can be used as feature extractors, giving a new representation with mutually independent dimensions. The number of filters (D) extracted by ICA is less or equal to the input data dimension (Δ). This can be reduced using a PCA previously to the ICA. The responses of the D filters to some pixels (p1 , . . ) are thus independent realizations of the D-dimensional random vector Y . As a consequence, the density can be factorized as expected: D D wi ∗ I(pt ) hiica (I(py )) = hica (I(pt )) = i=1 (19) i=1 Where ∗ is the convolution product.
Advanced Concepts for Intelligent Vision Systems: 13th International Conference, ACIVS 2011, Ghent, Belgium, August 22-25, 2011. Proceedings by David Van Hamme, Peter Veelaert, Wilfried Philips (auth.), Jacques Blanc-Talon, Richard Kleihorst, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)