By Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)
This publication constitutes the completely refereed lawsuits of the 14th foreign convention on complicated innovations for clever imaginative and prescient platforms, ACIVS 2012, held in Brno, Czech Republic, in September 2012. The forty six revised complete papers have been rigorously chosen from eighty one submissions and take care of photograph research and machine imaginative and prescient with a spotlight on detection, attractiveness, monitoring and identification.
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Additional info for Advanced Concepts for Intelligent Vision Systems: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012. Proceedings
5, in which the extrinsic parameters are considered. By comparing Fig. 5 with Fig. 4, the validity of our model as well as the depth calculation algorithm is shown. 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.
The extrinsic systematic errors are generated by multiple light reception. On the one hand, there is light scattering, caused by imperfect optics and inter-reflections within the camera. This phenomenon is significant in case that there are large amplitude differences in the image, whereby the distance measurements with low amplitudes are affected by distance measurements with stronger amplitudes. The reasons are either large distances between foreground and background objects or large viewing angles.
See Sec. 5. Finally, the view with the highest information gain is selected after simulating each candidate viewpoint, see Sec. 6. 0 ms. 40 S. Foix et al. Algorithm 1. Autonomous active view planning M ← ACTIVE V IEW P LANNING(x0 , Σ s , O, S) I NPUTS : x0 : Initial sensor pose in global coordinates. Σ s : Sensor measurement covariance matrix. O: 3D occupancy grid. S: A set of n measurements. O UTPUT: M: Task-based representation. 1: 2: 3: 4: 5: 6: 7: 8: 9: i=0 repeat Si ← DATA ACQUISITION(xi) (M, O) ← U PDATE R EPRESENTATIONS(S) cm ← V IEWPOINTS G ENERATION(xi, M) xi+1 ← D ECISION M AKER(cm, O, Σ s ) i=i+1 until task completed return M 5 Viewpoint Generation In order to determine the following viewpoint accordingly to the information gain, a search space consisting of multiple viewpoints (possible sensor positions and orientations) is required as input.
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.)