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
... 1 Learning Visual Landmarks for Mobile Robot Topological Navigation 33 Fig. 1 .22 . RWI B -2 1 test robot and laboratories and computer vision system Within the Systems Engineering and Automation ... (b) test 2 26 M. Mata et al. convergence, but local details allow a more precise one. A trade-off be- tween both kinds of details will achieve the best results. The individu...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 4 potx
... optical flow for both the GDIM-based and BCM-based method. A visual comparison of the Fig. 2. 6(c) with Figs. 2. 8(a) and 2. 8(b) reveals that the image motion field estimated using GDIM method ... kernel. 2. 5.1.3 2- D Exponential Chirp Transform Given a 2- D function f(x, y) and an invertible and differentiable trans- form ),(),(: K [ o: yx , the 2- D ECT is defined by th...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 7 pot
... on reinforcement learning and a neural network”, in Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, 20 02, vol. 3, pp. 26 72 26 77. 5 D. Zhou and K. H. Low, “Combined ... no. 4, pp. 23 7 28 5, 1996. 12 R. A. Brooks, “A robust layered control system for a mobile robot , IEEE Journal of Robotics and Automation, RA -2 ( 1), pp. 14 23 , 1986....
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 10 potx
... = e 2 0 + e 2 1 − e 2 2 − e 2 3 2( e 1 e 2 − e 0 e 3 )2( e 1 e 3 − e 0 e 2 ) 2( e 1 e 2 + e 0 e 3 ) e 2 0 − e 2 1 + e 2 2 − e 2 3 2( e 2 e 3 − e 0 e 1 ) 2( e 1 e 3 − e 0 e 3 )2( e 2 e 3 + e 0 e 1 ) e 2 0 − ... model. 2 The 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 s...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 11 pot
... 14 (2) :23 9 25 6, 19 92. [9] F. Blais and M. Rioux. Real-time numerical peak detector. Signal Processing, 11:145–155, 1986. [10] J-D Boissonnat. Geometric structure for three-dimensional shape rep- resentation. ... pages 24 2 24 8, 1998. [ 12] Y. Chen and G. Medioni. Object modeling by registration of multi- ple range images. In IEEE International Conference on Robotics and Automatio...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 12 potx
... Dimensional Model These 2- D features are the input to the 3-D blob estimation framework used by Azarbayejani and Pentland [4]. This framework relates the 2- D distribu- C. R. Wren 28 2 c1 c2 c3 c4 o1 o2 ... of 3-D parameters from calibrated sets of 2- D parameters is computationally very efficient, requiring only a small fraction of computational power as compared to the low-level segme...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 3 pps
... Barber, R., Salichs, M.A., (20 02) “Self-generation by a mo- bile robot of topological maps of corridors”. IEEE International Con- ference on Robotics and Automation, pp. 26 6 2- 2 667. 17 Escalera A. ... (20 00) “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 utilit...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 5 ppt
... inertial and visual mechanisms in the stabilization of gaze in natural and artificial systems, From: Motion Vision Computational, Neural, and Echological Con- straints, Eds. J. M. Zanker and ... 1990, pp .27 8 28 5. 25 . B.R. Friden and C. Oh, “Integral logarithmic transform : Theory and ap- plications”, Applied Optics, vol. 15, pp. 1138–1145, March, 19 92. 26 . J.J. Clark, M....
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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. 029 0 -9 6% 1. 027 8 10.5% 1.14 0. 128 -8 9.3% 1.061 -7 .0% 1.31 0. 121 3 -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 ... ZZ S ZZZZZZ S Z Z dF X X X L dFXdFXdF L e T T 2 2 2 2 2 2 )( 1 )1( 2 ))()()(( 2 ~ ³ ³³³ : :: : '' I...
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