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
... Annual Symposium CPM’98. pp. 9 1-1 03 37 Ohyama, T; (1995) “Neural network-based regions detection”. IEEE International Conference on Neural Networks. Proceedings, vol .3, nº 2; pp. 22 2 -3 02. 38 ... Proceedings of the SPIE- the International Society for Optical Engineering, vol. 35 25, pp. 32 6 -3 37 . 10 Blaer, P., Allen, P. (2002) “Topological mobile robot localization us-...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 13 ppsx
... Pentland and Horowitz employed non-rigid finite element models driven by optical flow in 1991 [35 ], and Metaxas and Terzopolous’ 19 93 system used deformable superquadrics [26, 30 ] driven by 3- D point and ... mechanisms allow the development of rich, full- body human-computer interface systems. These systems are applicable to desk- and room-scaled systems, theatrical devices, ph...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 14 ppsx
... behaviors and social expectancies in 3- month-old infants. Prosocial behavior was characterized as infants' positive behavior and 33 0 H. Asai et al. Additionally, off-line learning prevents ... 1999. [35 ] A. Pentland and B. Horowitz. Recovery of nonrigid motion and structure. IEEE Trans. Pattern Analysis and Machine Intelligence, 13( 7): 730 –742, July 1991. C. R....
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 15 pps
... Mecha- nism of Neural Cross-supramodal Integration based on Spatial and Temporal Properties of Attention. Neurocomputing, 5 2-5 4, 64 3- 6 48, 20 03. 24 Tikhonov, A.N., On Solving ill-posed problem and ... Engi. Syst., 5, 2-1 4, 2001. 27 Hamza, A.B., Krim, H. and Unal G.B., Unifying probabilistic and variational estimation. IEEE Signal Processing Magazine, 3 7-4 7,2002....
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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-21 test robot and laboratories and computer vision system Within the Systems Engineering and Automation ... 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, etc....
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 4 potx
... Mueller R. Etienne-Cummings, J.Van der Spiegel and Mao-Zhu Zhang, “A foveated silicon retina for two-dimensional tracking”, IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Processing, ... spatial domain vision op- erations. Next section presents classic vision algorithms (space-variant op- tical flow, stereo disparity, anisotropic diffusion, corner detection and et...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 5 ppt
... the Win- dowed Fourier Transform. We will give a brief and concise review on Fou- rier Transform and Wavelet Transform. 3. 3 .3. 1 Short-Time Fourier Transform and Wavelet Transform The Short-Time ... STFT. 3. 3 .3. 2 Discrete Wavelet Transform Wavelet Transform (WT) is a technique for analyzing signals. It was de- veloped as an alternative to STFT to overcome problems related to i...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 6 pdf
... CBMS-NSF conference, Wavelets Applications, Lowell, MA, USA. 6 Desantis PM (1994) Path-tracking for a tracker-trailer-like robot, The International Journal of Robotics Research, pages 53 3- 5 43. ... Length (m) )( ˆ mL error )( ˆ mL error 0. 93 0.0290 -9 6% 1.0278 10.5% 1.14 0.128 -8 9 .3% 1.061 -7 .0% 1 .31 0.12 13 -9 0% 1.415 7.9% 1.46 0.1577 -8 9% 1.50 2.6% 4 Con...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 7 pot
... vol. 38 , no. 3- 4 , pp. 137 –148, 2002. 8 Z. Kalmar, C. Szepesvari, and A. Lorincz, “Module-based reinforcement learning: Experiments with a real robot , Autonomous Robots, vol. 5, pp. 2 73 295, ... Cybernetics, Part B (Cybernetics), vol. 33 , no. 1, pp. 17–27, 20 03. 7 T. Belker, M. Beetz, and A. B. Cremers, Learning action models for the improved execution of navigation p...
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