By Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)
This ebook constitutes the completely refereed court cases of the 14th overseas convention on complex 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 snapshot research and desktop imaginative and prescient with a spotlight on detection, reputation, monitoring and identification.
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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
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. 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.
Alternatively, our proposed algorithm (Fig. 6c) presents a good model compromise between model precision and time acquisition for most robotic tasks. Our approach takes explicit advantage of the noisy and low resolution data obtained from a 3D ToF camera. ) and with less complexity in the setup. Note that with this algorithm an explicit uncertainty measure of each point of the model is maintained, and therefore it is easy to define measures of the overall quality on the model. One natural consequence of the algorithm is that for complex objects with concavities and details, a higher number of view points for completing the Information-Gain View Planning (a) Pre-defined trajectory with SR3000 3D ToF camera.
Finally, the Camel has a big concavity under its legs and a very heterogeneous structure due to its neck and head. 1 Setup The current approach has been tested using a 7 DoF manipulator robot type Kuka KR16 and a 3D ToF camera type Swissranger SR4000 attached to its flange. The 3D ToF camera is attached in a 90 degree angle with respect to the tool-center-point (TCP). During the experiments, the objects were placed on a fixed and static platform at a height of approximately 670 mm. At this height, and due to its wide workspace, the Kuka KR16 manipulator is able to cover comfortably the surrounding volume of a medium-sized 44 S.
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.)