Download e-book for kindle: RoboCup 2010: Robot Soccer World Cup XIV by Andreas Seekircher, Tim Laue, Thomas Röfer (auth.), Javier

By Andreas Seekircher, Tim Laue, Thomas Röfer (auth.), Javier Ruiz-del-Solar, Eric Chown, Paul G. Plöger (eds.)

ISBN-10: 3642202160

ISBN-13: 9783642202162

ISBN-10: 3642202179

ISBN-13: 9783642202179

This booklet comprises the completely refereed post-conference complaints of the 14th RoboCup foreign Symposium, held in Singapore, in June, 2010 - representing the clinical tracks based in 4 periods entitled simulation and rescue robots; robotic notion and localization; robotic movement and humanoid robots; and human robotic interplay and semantic scene research. The 20 revised complete papers and sixteen revised brief papers awarded have been rigorously reviewed and chosen from seventy eight submissions. Documenting the examine advances of the RoboCup neighborhood because the predecessor symposium, this publication constitutes a important resource of refrence and concept for R&D execs attracted to RoboCup or in robotics and disbursed AI extra generally.

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Extra info for RoboCup 2010: Robot Soccer World Cup XIV

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Face Recognition: A Literature Survey. ACM Computing Surveys, 399–458 (2003) 3. : Face recognition from a single image per person: A survey. Pattern Recognition 39, 1725–1745 (2006) 4. : Human and Machine Recognition of Faces: A Survey. Proceedings of the IEEE 83(5), 705–740 (1995) 5. org/ 6. : Eigenfaces for Recognition. Journal of Cognitive Neurosicence 3(1), 71–86 (1991) 7. : Recognition of Faces in Unconstrained Environments: A Comparative Study. EURASIP Journal on Advances in Signal Processing (Recent Advances in Biometric Systems: A Signal Processing Perspective) 2009, article ID 184617, 19 pages (2009) 8.

In (a) and (b) the images corresponds to the same person, while in (c) and (d) they correspond to different persons. 32 G. Hermosilla, P. Loncomilla, and J. 2 Recognition Results Cross-validation is used for evaluating the performance of the different methods in the database. From the set of categories {V1, V2, V3, E1, E2, E3} all the possible pairs of categories (A,B) are formed. For each pair (A,B), the images in A are selected as test images, while the images in B are selected as gallery images.

2 Face Segmentation A skin detection model in the thermal spectrum is created by modelling both, the skin pixel intensity distribution, and the non-skin distribution, as mixtures of Gaussians (MoG). Both the skin and non-skin intensity distributions are modelled as the mixture of four Gaussians: 4 pSKIN (x) = ∑ ω Si i=1 4 ⎛ 1 (x − μSi )2 ⎞ 1 exp ⎜ − ⎟ 2 2πσ Si ⎠ ⎝ 2 σ Si pNON −SKIN (x) = ∑ ω Ni i=1 ⎛ 1 (x − μ Ni)2 ⎞ 1 exp ⎜ − ⎟ 2 2πσ Ni ⎝ 2 σ Ni ⎠ (1) (2) A pixel is detected as skin when its intensity has a larger probability of belonging to the skin class than to the non-skin class.

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RoboCup 2010: Robot Soccer World Cup XIV by Andreas Seekircher, Tim Laue, Thomas Röfer (auth.), Javier Ruiz-del-Solar, Eric Chown, Paul G. Plöger (eds.)


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