By Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)
This publication constitutes the completely refereed court cases of the 14th foreign convention on complex ideas for clever imaginative and prescient platforms, 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 machine 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 info for Advanced Concepts for Intelligent Vision Systems: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012. Proceedings
Acknowledgment. Authors would like to thank DGA (French Direction G´en´erale de l’Armement ) and CNRS (Centre National de la Recherche Scientifique) for ﬁnancial support. References 1. : Pupil localization and tracking for video-based iris biometrics. In: Proc. of the 10th Int. Conf. on Information Sciences, Signal Processing and their Applications (ISSPA 2010), Kuala Lumpur, Malaysia, pp. 650–653 (2010) 2. : On the statistical analysis of dirty pictures (with discussion). J. of Royal Statistical Society 48(3), 259–302 (1986) 3.
Then the transformations are: Pe = [Re |Te ] Pω (2) Pc = [Rc |Tc ] Pe (3) Depth from Vergence and Active Calibration for Humanoid Robots 27 Deﬁne that an initial eye axes at start-up position are orthogonal to er − el . Then deﬁne the eye rotation matrices around the initial left eye axis and right eye axis as Rxt (ϕ) and Ryt (θ) at time t from start-up. Rxt (ϕ) is pan (left and right) rotation matrix which aﬀects the v pixels in image coordinates and Ryt (θ) is the tilt (up and down) rotation matrix which aﬀects the u pixels in image coordinates.
At the same time, each voxel is defined by three possible occupancy types: occupied, free or unknown. By using the covariance matrix as an uncertainty voxel-related measurement, our approach can optimally obtain the information gain taking into account the orientation of the sensor. This is an important feature when using a noisy sensor such as a 3D ToF camera, since the error is usually bigger on one component. 2 Expected Gain Using an Occupancy Grid Initially, all voxel states are set to unknown, state with the highest uncertainty.
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.)