By Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)
This ebook constitutes the completely refereed lawsuits of the 14th overseas convention on complex techniques 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 care for snapshot research and desktop imaginative and prescient with a spotlight on detection, acceptance, 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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Additional resources for Advanced Concepts for Intelligent Vision Systems: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012. Proceedings
3. 5. An example is given in Figure 6b, where the area in red ˆ shows the intersection of masks Mn , n ∈ [1, N ]. M This fusion strategy shows three typical types of areas –an eyelid region A, an eyelash and an eyelash reﬂection region B, and a pupil and iris region C– with low levels of conﬁdence: – Region A: Some pixels of the eyelid are not taken into account by the mask ˆ. M – Region B: Many pixels are gray-valued, indicating that the presence of eyelashes degrades the quality of the iris code in that area.
The optical centers of the new projection matrices are the same as the old ones, and also the intrinsic parameters are the same, whereas the new rotation matrices diﬀer from the old ones by a rotation. Given time t, deﬁne the old projection matrices as: H0t = A[R0t | − R0t ct ] (23) and new projection matrices as: Hnt = A[Rnt | − Rnt ct ] (24) In a 3D perspective, we can write t λ0 P˜im0 = H0t P˜w t λn P˜imn = Hnt P˜w (25) (26) There is only a very small diﬀerence between λ0 and λn ; so we can ignore it and assume λ = λ0 = λn .
4. 15 Fig. 4. Depth error vs. baseline and ground depth based on (14) Fig. 4, that with an increase of the baseline, the depth error decreases inversely, whereas the depth error increases with an increase of the ground truth depth. That is, the depth accuracy depends on the ground truth depth and the baseline. Note that, the increase of the baseline is at the cost of computation time. Also, in real robotic applications, the baseline determines the size of the robot’s head, which can not be increased without limitation.
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.)