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

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

... 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 a set ... region-growing method, projection method, and sculpting-based method. In the next sub-sections we will ex- plain each of these categories. 6.4.1 Volumetric Methods The volumetric method consists...

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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

... 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, ... high dy- namic performance, in a very compact and light design, with foveal and peripheral vision. The lenses provide the ability to globally observe the en- vironment and pr...

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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 12 potx

Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 12 potx

... 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 segmentation algorithms[5]. ... Motion Understanding the observation process is significantly non-trivial. Video images of the hu- man body are extremely high-dimensional signals and the mapping between body pose 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

... 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. Lo- cal details ... symbols: capital letters, small letters, and numbers and icons like emergency exits, stairs, elevators, fire extinguishing materials, etc. The weight sets are tried se- quentially until...

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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

... pp. 32 6-3 37. 10 Blaer, P., Allen, P. (2002) “Topological mobile robot localization us- ing fast vision techniques”. IEEE International Conference on Robot- ics and Automation, pp. 103 1-1 036. ... 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 artifi...

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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

... human visual system : functional and topological transformation performed in the periferal visual field”, Biological Cyber- netics, vol. 44, 1982. 102 Y. Sun et al. model of the robot manipulator ... 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....

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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

... discrete wavelet analysis will be used to decompose the raw signal into several signals with different bandwidths. This algorithm makes the signal, in this case, the raw angular velocity signal ... the levels. The esti- mation reaches the minimum %6.2 and %9.7%,5 .10 ' L L at level 11, 10 and 10, respectively. At the same level, the residual e ~ is also mini- mized. Thus,...

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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

... 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 towards the target. In the ... robot , IEEE Journal of Robotics and Automation, RA-2(1), pp. 14–23, 1986. 168 G. Unal et al. Usage of this high level semantic information yields robust algorithms at a high...

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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

... 194 G. 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 ... 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 evolution equat...

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