Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 8 pptx
Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 8 pptx
... Unal et al. Fig. 5.15. A walking person (Frames shown L-R-top-bottom) is tracked by the polygonal tracker 180 G. Unal et al. Fig. 5.5. Two rays-swimming video noisy version (Frames 1, 8, ... outward unit normal of edge (P kí1 í P k) (resp. (P k í P k+1)), and L parameterizes a line between P kí1 and P k or P k and P k+1. We note the similarity between this polygonal evolut...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 2 pot
... 4 8 12 15 20 0 93 91,5 87 ,5 84 71 29 15 90 88 ,5 86 78 63 18, 5 30 86 84 79,5 73 45,5 11,5 45 82 78, 5 73,5 60 25,5 0 60 77 72 56,5 32 12,5 0 75 65,5 52,5 37 16 0 0 1.6.1 Robot Localization ... global and/ or local object specific detail. Global details do not have a precise geometric location in- side the object, such as statistics of gray levels or colors, textures...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 3 pps
... image using genetic algorithms and neural networks”. Interna- tional Conference on Industrial Electronics, Control and Instrumenta- tion, pp. 183 8- 1 84 3. 2 Argamon-Engelson, S. (19 98) “Using image ... analysis and computer vision 1999 [sur- vey]”. Computer Vision and Image Understanding, vol. 78 nº 2, pp 22 2-3 02. 50 M. Mata et al. have shown its utility for both artif...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 4 potx
... coordinates”, in SPIE- Intelligent robot and computer vision VIII: Algoritms and techniques,, 1 989 , pp. vol 9 38, pp .85 4 86 6, Belling- ham,WA. 3. C. Capurro, F. Panerai and G. Sandini, “Dynamic vergence ... coordinates”, in the SPIE- Intelligent robot and computer vision VIII: Algoritms and techniques, vol 9 38, pp.13 8- 1 45, SPIE, Bel- lingham,WA 1 989 , 19 98. 2 Fove...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 5 ppt
... human visual system : functional and topological transformation performed in the periferal visual field”, Biological Cyber- netics, vol. 44, 1 982 . 102 Y. Sun et al. model of the robot manipulator ... vol. 244, pp. 87 –90, 1991. 88 . B.K.P Horn, “Relative orientation”, International Journal of Computer Vision, vol. 4, pp. 59– 78, 1990. 89 . G. Salgian and D.H. Ballard, “Visu...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 6 pdf
... Length (m) )( ˆ mL error )( ˆ mL error 0.93 0.0290 -9 6% 1.02 78 10.5% 1.14 0.1 28 -8 9.3% 1.061 -7 .0% 1.31 0.1213 -9 0% 1.415 7.9% 1.46 0.1577 -8 9% 1.50 2.6% 4 Continuous Reinforcement Learning Algorithm for Skills Learning in an ... discrete wavelet analysis will be used to decompose the raw signal into several signals with different bandwidths. This algorithm ma...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 7 pot
... Autonomous Systems, vol. 38, no. 3-4 , pp. 137–1 48, 2002. 8 Z. Kalmar, C. Szepesvari, and A. Lorincz, “Module-based reinforcement learning: Experiments with a real robot , Autonomous Robots, vol. 5, ... consists of the robot going towards a goal which is a visual target. First of all, the robot moves the turret to center the target on the image and then the robot moves t...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 9 doc
... 12–49, 1 988 . 45. D. Peng, B. Merriman, S. Osher, H-K. Zhao, and M. Kang, “A PDE- based fast local level set method,” J. Computational Physics, vol. 255, pp. 410–4 38, 1999. 46. T.F. Chan and L.A. ... 48. M. Bertalmio, L.T Cheng, S. Osher, and G. Sapiro, “Variational problems and partial differential equations on implicit surfaces,” J. Computational Physics, vol. 174, no. 2, pp...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 10 potx
... and McKay [8] , and it has become the principle technique for registration of 3D data sets. The algo- rithm takes two 3D data sets as input. Let P and Q be two input data sets containing N p and ... α-shape of a finite point set S is a polytope uniquely determined by S and the param- eter α that controls the level-of-detail. A subset T ⊆ S of size |T | = k +1with 0 ≤ k ≤ 2 belongs to...
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