Download Advanced Concepts for Intelligent Vision Systems: 14th by Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, PDF

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 foreign convention on complex innovations for clever imaginative and prescient structures, 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 picture research and machine imaginative and prescient with a spotlight on detection, attractiveness, 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

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Figure 4 shows the final result of each of the objects used in three of our experiments. 8 Conclusions and Future Research The paper presents a hybrid active view planning approach and its application to autonomously modelling an unknown free-form 3D object using a noisy sensor. The method combines viewpoint generation and viewpoint selection based on evaluation of the information gain. The method has been evaluated experimentally using a calibrated 3D ToF camera and a robotic arm on the task of 3D object modelling.

0 ⎠ 0 · · · 0 Σn Finally, the overal expected information gain is computed as 3n IG = log(Aii ). (6) i=0 7 Experimentation In order to be evaluated, the proposed active view planning has been applied to the task of modelling three free-form objects (see Fig. 4). Although all objects have similar sizes, each of them has its own degree of complexity, mainly based on the number of concavities. The most complex object is the Camel, followed by the Zeus and the Mozart bust. On the Mozart bust the only influential concavity is the one at its neck.

In [17] objects are represented statistically by multidimensional receptive field histograms, and the camera is controlled by making hypotheses on the salient points of the previously learned objects and then moving to the most discriminative viewpoint. In [3] reinforcement learning is used to associate the current state with camera actions and their corresponding reward. Here the model is a particle representation, and it is updated with new sensor readings with the Condensation algorithm. More recently, a boost-based algorithm to combine different appearance estimators [9] has been proposed to compute the next view in a rotating object framework.

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