Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 1 pptx

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

... 19 4 G. Unal et al. Fig. 5 .15 . A walking person (Frames shown L-R-top-bottom) is tracked by the polygonal tracker 18 0 G. Unal et al. Fig. 5.5. Two rays-swimming video noisy ... prediction using optical flow normal component Fig. 5 .14 . A swimming fish in a rocky terrain in the sea (Frames 1, 10 , 20, 30, 40, 70, 11 0, 14 3 are shown left-right-top-bottom). Rows 1 and...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 2 pot

Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 2 pot

... pattern-windows follows them, as shown in Fig. 1. 11. c, as equation (7) indicates. The pattern-windows will 38 M. Mata et al. Fig. 1. 27. Landmark map of room 1. 3C13 and room sweep with detected land- marks Fig. ... 1 Learning Visual Landmarks for Mobile Robot Topological Navigation 33 Fig. 1. 22. RWI B- 21 test robot and laboratories and computer vision system Wi...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 3 pps

Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 3 pps

... vol. 21, pp. 24 7-2 58. 18 Fahlman, S. E. (19 98) “An empirical study of learning speed in back- propagation networks”. CMU-CS-8 8 -1 62. 19 Franz, Matthias O. (19 98) Learning view graphs for robot ... recognition [9, 10 , 11 ] and object tracking [12 , 13 , 14 ], one usually needs a real-time coordination between sensory perception and motor control [15 ]. A biological...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 4 potx

Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 4 potx

... “Tracking algorithms for log-polar mapped image coordinates”, in the SPIE- Intelligent robot and computer vision VIII: Algoritms and techniques, vol 938, pp .13 8 -1 45, SPIE, Bel- lingham,WA 19 89, 19 98. ... understanding, vol. 56 (1) , pp. 10 8 12 9, 19 92. 9. S.S Young, P.D. Scott and C. Bandera, “Foveal automatic target recogni- tion using a multiresolution neural network...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 5 ppt

Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 5 ppt

... Schwartz, “The local structure of space- variant images”, Neural Network, vol. 10 (5), pp. 815 –8 31, 19 97. 3. On-line Model Learning for Mobile Manipulations 10 7 211 211 cossin 1 sincos 1 vvf m y vvf m x cc c c cc c c  ... pp. 411 –435, 19 80. 86. J.M. Foley, “Binocular distance perception: Egocentric visual task”, Journal of experimental Psychology, vol. 11 , pp. 1...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 6 pdf

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. 0278 10 .5% 1. 14 0 .12 8 -8 9.3% 1. 0 61 -7 .0% 1. 31 0 .12 13 -9 0% 1. 415 7.9% 1. 46 0 .15 77 -8 9% 1. 50 2.6% 4 Continuous Reinforcement Learning Algorithm for Skills Learning in an ... On-line Model Learning for Mobile Manipulations 13 1 0 2 4 6 8 10 12 14 0 0.5 1 1.5 2 level estimation e...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 7 pot

Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 7 pot

... International Conference on Robotics and Automation, pp. 11 24, 11 29, 20 01. 10 T. Mitchell, Machine Learning, McGraw Hill, 19 97. 11 L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement learning: ... 15 02 15 08, November 19 98. 51 A.J. Smith, “Applications of the self-organizing map to reinforcement learning , Neural Network, vol. 15 , no. 8-9 , pp. 11 07...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 9 doc

Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 9 doc

... combining these two sets of equations, and by setting x i − ρx i =0,we obtain t 11 u 1 + t 12 v 1 + t 13 − t 41 u 1 x 1 − t 42 v 1 x 1 − t 43 x 1 =0 t 11 u 2 + t 12 v 2 + t 13 − t 41 u 2 x 2 − t 42 v 2 x 2 − ... 309–354, 19 84. 212 .75 212 .48 211 .98 1 1 1 339 340 3 41 100 200 Image Row Sequence of images 10 0th image 200th image 480 340 1 640 1...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 10 potx

Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 10 potx

... Range and Intensity Images 237 5 012 34 6789 10 11 12 13 14 (a) (c) (d) (b) (e) −0.8 +0 .15 −0.8 +0 .15 +0.45 +0.85 +0.3 +0.3 −0.65 −0.65 1. 5 1. 5 +0.45 +0.85 −0.3 −0.3 +0.7 +0.7 6.4.4 Projection Methods The ... resulting model. The to- tal number of points and triangles in the 40 range images were 1, 6 01, 563 and 3,053,907, respectively, and these were reduced to 14 8,...
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