By Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)
This booklet constitutes the completely refereed lawsuits of the 14th overseas convention on complicated thoughts for clever imaginative and prescient structures, ACIVS 2012, held in Brno, Czech Republic, in September 2012. The forty six revised complete papers have been conscientiously chosen from eighty one submissions and take care of picture research and computing device imaginative and prescient with a spotlight on detection, attractiveness, monitoring and identification.
Read Online or Download Advanced Concepts for Intelligent Vision Systems: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012. Proceedings PDF
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Extra resources for Advanced Concepts for Intelligent Vision Systems: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012. Proceedings
Comparing with the standard image rectiﬁcation method that transforms each image plane such that pairs of conjugate epipolar lines become collinear and parallel to one of the image axis (usually the horizontal one), our calibration method makes use of it to transform the image planes such that they become virtually orthogonal to the eye axes. The diﬀerences lie in that the main goal of image rectiﬁcation is to reduce the stereo searching problem from 2D to 1D and it is for ﬁxed set-up. Our calibration method extends the idea of image rectiﬁcation and it does not involve a stereo matching concern, and it is used for active vision system.
2 Experiment 2 Experiment 2 investigates the inﬂuence of the object positioning accuracy on the depth error. First, we introduce the deﬁnition of positioning accuracy. Given the image center, we track the projected image point of an object inside a window of the size ε around the image center. We call ε the positioning accuracy. The smaller ε, the more accurate the positioning. As shown in Fig. 6, the depth error drops signiﬁcantly when the positioning accuracy increases 10 times, whereas after this threshold, the improvement of 34 X.
Depending on whether a voxel is crossed by a ray-trace or whether it encloses a new measurement, the voxel state is set to free or to occupied, respectively. Also, each occupied voxel is assigned its measurement covariance matrix Σ i in order to posteriorly compute the information gain of new viewpoints. If the voxel was previously defined occupied, both the new covariance matrix and the former are combined as shown in Fig. 3 by (Σi )−1 = (Σit−1 )−1 + (Σit )−1 . (4) Only voxels with unknown and occupied states would be considered for estimating the information gain, since free voxels do not provide any information.
Advanced Concepts for Intelligent Vision Systems: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012. Proceedings by Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)