bite mark analysis part 2 adobe photoshop®
Lagrangian analysis and prediction of coastal and ocean dynamics a griffa, et al , (cambridge, 2007) WW
... coastal and ocean dynamics (LAPCOD) 39 68 89 119 136 1 72 204 23 1 27 5 349 401 423 480 Index The color plates are situated between pages 22 8 and 22 9 v Contributors Luca R Centurioni Scripps Institute ... hydrography J Atmos Ocean Tech., 21 , 29 8–316 Pochapsky, T E., 1963 Measurement of small-scale oceanic motions with neutrally buoyant floats Tellus, 15, 3 52 62 Prater, M D., 20 02 Eddies in the Labrador ... 3 62) Chapel Hill, NC 27 599 USA viii List of contributors Matthias Lankhorst Leibniz-Institut fur Meereswissenschaften (IFM-GEOMAR) Dusternbrooker Weg 20 Kiel D -24 105 Germany Dong-Kyu Lee Department...
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coma science - clinical and ethical implications - s. laureys, et al., (elsevier, 2009)
... determinations? Neurology, 70, 25 2 25 3 Bernat, J L (20 08b) Ethical issues in neurology In (3rd ed., pp 26 6 26 9) Philadelphia, PA: Lippincott, Williams & Wilkins Bernat, J L (20 09) Brain death In S Laureys ... pathways in man Nature, 24 3, 29 5 29 6 Ramsøy, T Z., & Overgaard, M (20 04) Introspection and subliminal perception, phenomenology and the cognitive sciences, 3(1), 1 23 Schiff, N D (20 05) Modeling the ... Proceedings of the National Academy of Sciences, USA, 92, 6 122 –6 126 S Laureys et al (Eds.) Progress in Brain Research, Vol 177 ISSN 0079-6 123 Copyright r 20 09 Elsevier B.V All rights reserved CHAPTER...
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cocaine abuse - behavior, pharmacology and clin. applns - p. dews, et al., (ap, 1998)
... (0 o ^ 20 20 lio^ lio CSF10 25 50 100 * - T r I— CSF10 20 0 25 50 100 20 0 CSF10 401 I 25 I 50 I 100 I 20 0 ' r r CSF10 25 50 100 20 0 40' c 30- c CA 30H CO ^ 20 .fi ^ 2o^ lio llOH CSF10 25 I • i ... Baltimore, Maryland 21 224 Maxine L Stitzer (363), Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland 21 224 Sharon L Walsh (20 9), Behavioral ... Z SCH23390: HR SESSION SCH23390: FIRST 20 MIN Z Op 25 01 • AccSh CeA m CPU 25 0 20 0 i 150 C UJ D ™ -J fc CO LU O ^ 20 0 150 00 t ' l • AccSh • CeA m CPU 100 X a: < LU O Q-O o TOTAL DOSE SCH23390...
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Báo cáo y học: "Response to commentary by Dixon and Silman on the systematic review and meta-analysis by Bongartz et al" doc
... review and meta -analysis of rare harmful effects in randomized controlled trials JAMA 20 06, 29 5 :22 75 -22 86 Stern R, Wolfe F: Infliximab dose and clinical status: results of studies in 16 42 patients ... Rheumatol 20 04, 31:1538-1545 Remicade label information [http://www.remicade.com/global/ hcp/hcp_PI.jsp] Lagakos SW: Time-to-event analyses for long-term treatments: The APPROVe trial N Engl J Med 20 06,...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 2 pot
... distance angle (m) ( ) 15 30 45 60 75 12 15 20 93 90 86 82 77 65,5 91,5 88,5 84 78,5 72 52, 5 87,5 86 79,5 73,5 56,5 37 84 78 73 60 32 16 71 63 45,5 25 ,5 12, 5 29 18,5 11,5 0 1.6.1 Robot Localization ... Fig 1 .27 Landmark map of room 1.3C13 and room sweep with detected landmarks Fig 1 .28 Landmark signature for room 1.3C13 The obtained landmark sequence (room signature) is presented in Fig 1 .28 , ... 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 department in...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 3 pps
... Statistics and Computing, vol.8, nº 3, pp .22 1 -22 8 42 Salichs, M.A., Moreno, L (20 00) “Navigation of mobile robots: open questions” Robotica, vol.18, pp 22 7 -23 4 43 Selinger A., Nelson R C (1999) “A ... (20 00) “Image analysis and computer vision 1999 [survey]” Computer Vision and Image Understanding, vol 78 nº 2, pp 22 2-3 02 54 M Mata et al 41 Rue H and Husby O.K (1998) “Identification of partly ... IFSA World Congress and 20 th NAFIPS International Conference, vol 4, pp 24 22- 2 427 27 Launay, F., Ohya, A., Yuta, S (20 02) “A corridors lights based navigation system including path definition...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 4 potx
... side to the white vertical line (see Figs 2. 11(b), 2. 12( b) and 2. 13(b)) were used for the computation of optical flow Figures 2. 11(a)–(c), 2. 12( a)–(c) and 2. 13(a)–(c) shows a sample frame, log-polar ... angular directions 2 Foveated Vision Sensor and Image Processing – A Review 81 2. 6 .2. 1 Synthetic Image Sequences The first image is that of a textured 25 6 × 25 6 face image (see Fig 2. 7(a)) Using a ... computed image motion using GDIM-based method Fig 2. 12: Similar results as shown in Fig 2. 11 using an outdoor scene From Figs 2. 11(c) and 2. 12( c) it is clear that the flow distributions for the...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 5 ppt
... vol 12, no 1, pp 43–77, February 1994 M Yeasin, “Optical flow on log-mapped image plane: A new approach”, in the lecture notes on computer science, Springer-Verlag, NY, USA, Feb 20 01, pp 25 2 26 0 ... log-mapped image plane - A new approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 24 , pp 125 –131, 20 02 78 D Todorovic, “A gem from the past: Pleikart stumpf’s anticipation ... in Proc of Royal Stat Soc., London, B 221 , 1984, pp 189 22 0 81 J.A Movshon, E.H Edelson, M.S Gizzi and W.T Newsome, “Pattern recognition mechanisms”, In the analysis of moving visual patterns:...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 6 pdf
... Estimate Length Estimate 129 0.5 10 15 Time 0.5 30 25 20 15 Time 20 25 30 10 15 Time 20 25 30 0.8 0.6 10 0.8 0.6 p p 0.4 0.4 0 .2 0 .2 0 15 Time 10 25 20 30 (11th level) (12th level) th th Fig 3.10 ... Estimate Length Estimate 0.5 0 10 15 Time 20 25 0.5 0 30 0.8 10 15 Time 20 25 30 15 Time 20 25 30 0.8 0.6 10 0.6 p p 0.4 0.4 0 .2 0 .2 0 10 15 Time th 20 (9 level) 25 30 0 th (10 Level) th th Fig 3.9 ... 25 30 130 Y Sun et al 1.4 4.5 1 .2 3.5 Length Estimate Length Estimate(m) 0.8 0.6 0.4 2. 5 1.5 0 .2 0.5 0 10 15 20 30 25 0 10 Time(s) 15 20 25 30 Time(s) th th (11 level) ( 12 level) th th Fig 12...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 7 pot
... Cybernetics, Part C, vol 33, no 4, pp 4 92 501, 20 03 37 W A Wright, “Learning multi-agent strategies in multi-stage collaborative games”, in IDEAL 20 02, vol 24 12 on Lecture Notes in Computer Science, pp 25 5 26 0, ... and Systems, 20 02, vol 3, pp 26 72 26 77 D Zhou and K H Low, “Combined use of ground learning model and active compliance to the motion control of walking robotic legs”, in Proceedings 20 01 ICRA IEEE ... Intelligent Research, , 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 4 Reinforcement...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 8 pptx
... Fig 5.4 Two rays are swimming gently in the sea (Frames 1, 10, 15, 20 , 22 , 23 , 24 , 69 are shown left-right top-bottom) Rows and 2: Tracking without prediction Rows and 4: Tracking with prediction ... rays-swimming video noisy version (Frames 1, 8, 13, 20 , 28 , 36, 60, 63 are shown) Tracking with prediction using optical flow orthogonal component 5 .2. 2 Continuous Tracker with Smoothness Constraint ... given on the right (with =0.1, a time step of 0 .24 and number of iterations=400) Fig 5.7 Two rays swimming video noisy version (Frames 1, 8, 13, 20 , 28 , 36, 60, 63 are shown) Tracking with prediction...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 9 doc
... 0, we obtain t11 u1 + t 12 v1 + t13 − t41 u1 x1 − t 42 v1 x1 − t43 x1 = t11 u2 + t 12 v2 + t13 − t41 u2 x2 − t 42 v2 x2 − t43 x2 = t11 un + t 12 + t13 − t41 un xn − t 42 xn − t43 xn = (11) Since ... 0 0 1 −u1 x1 −u2 x2 −un xn −u1 y1 −u2 y2 −un yn −u1 z1 −u2 z2 −un zn −v1 x1 −v2 x2 −vn xn −v1 y1 −v2 y2 −vn yn −v1 z1 −v2 z2 −vn zn 72 7 76 76 76 76 76 76 76 76 ... Converting a sequence of images into a range image 21 0 J Park and G N DeSouza 25 0 intensity 20 0 150 100 50 25 0 25 5 26 0 26 5 27 0 column 27 5 28 0 28 5 29 0 Fig 6.3: Typical intensity distribution around...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 10 potx
... e0 e1 e2 e3 where e0 ≥ and e2 + e2 + e2 + e2 = Then, the rotation matrix R is given by 2 (e1 e3 − e0 e2 ) e0 + e2 − e2 − e2 (e1 e2 − e0 e3 ) R = (e1 e2 + e0 e3 ) e2 − e2 + e2 − e2 (e2 e3 ... as d2 (p, q) = d2 (p, q) + d2 (p, q) e c (22 ) where de (p, q) = dc (p, q) = (xp − xq )2 + (yp − yq )2 + (zp − zq )2 , λ1 (rp − rq )2 + 2 (gp − gq )2 + λ3 (bp − bq )2 (23 ) (24 ) and λ1 , 2 , ... Range and Intensity Images 23 9 V1 V2 V3 S = V1 V2 U U U U U U S = V1 U U V3 S = V1 V2 V2 U V3 U V2 U S = V1 V2 U S = V1 V2 U S = V1 V3 V3 V3 U S = V1 V2 V3 V3 Fig 6 .20 : Redundant surfaces of three...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 11 pot
... I2 I2,R I2,G I2,B M= = In In,R In,G In,B range images (45◦ interval) and 120 color images (3◦ interval) were acquired in the example presented in their paper 25 6 ... 3(4) :26 6 28 6, 1984 [11] C Chen, Y Hung, and J Chung A fast automatic method for registration of partially-overlapping range images In IEEE International Conference on Computer Vision, pages 24 2 24 8, ... Modeling, pages 179–186, 20 01 [21 ] G Godin, M Rioux, and R Baribeau 3-D registration using range and intensity information In SPIE Videometrics III, pages 27 9 29 0, 1994 [22 ] M Hebert, K Ikeuchi,...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 12 potx
... of Σs : | | | | λ1 (31) Σs L1 L2 = L1 L2 2 | | | | Without loss of generality, λ1 ≥ 2 , and L1 = L2 = With those constraints, λ1 and 2 represent the squared length of the semi-major ... u = E[ut ] (21 ) T ¯ ¯ U = E[(ut − u)(ut − u) ] (22 ) If the control covariance matrix is small relative to the model and observation noise, by which we mean: U U
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 13 ppsx
... 0.6 0.4 0 .2 −0 .2 −0.4 0.5 Player 0.5 100 20 0 300 400 500 100 20 0 300 400 500 100 20 0 300 400 500 1 2 likelihood 3 100 20 0 300 400 500 100 20 0 300 400 500 frame 100 20 0 300 400 500 Fig 7 .28 : Top: ... Grab Wave Whip Brush 20 10 −10 10 left z left y 20 20 10 −10 right y right x −10 20 10 right z −5 −10 head x 10 −10 head y 25 20 15 head z 10 20 10 20 15 10 time Fig 7 .25 : A pessimal example ... Open Wave Whip Brush 20 10 −10 10 left z left y 20 20 10 −10 right y right x −10 20 10 right z −5 −10 head x 10 −10 head y 25 20 15 head z 10 20 10 10 time 15 20 Fig 7 .26 : The recursive tracker...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 14 ppsx
... payoff(1,1) 0.5 payoff(1,1) payoff(1 ,2) payoff(1 ,2) payoff(1 ,2) 0.5 payoff(1 ,2) 0 0.5 payoff(1 ,2) 0.5 payoff(1,1) payoff(1 ,2) 0.5 payoff(1 ,2) payoff(1 ,2) payoff(1 ,2) payoff(1 ,2) 0.5 0 0.5 payoff(1,1) Fig ... payoff(1,1) 0.5 payoff(1,1) payoff(1 ,2) payoff(1 ,2) payoff(1 ,2) 0.5 payoff(1 ,2) 0 0.5 payoff(1 ,2) 0.5 payoff(1,1) payoff(1 ,2) 0.5 payoff(1 ,2) payoff(1 ,2) payoff(1 ,2) payoff(1 ,2) 0.5 0 0.5 payoff(1,1) Fig ... 0.5 0.5 payoff(1,1) 0.5 payoff(1,1) 1 0.5 payoff(1 ,2) payoff(1 ,2) 0.5 payoff(1,1) payoff(1 ,2) 0.5 payoff(1 ,2) payoff(1 ,2) payoff(1 ,2) payoff(1 ,2) 339 0.5 0.5 payoff(1,1) payoff(1,1) Fig 8.6 A simulation...
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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 15 pps
... altruism ? Ethol Sociobiol, Vol.9, 21 1 -22 2, 1988 Nesse, R.M., Evolutionary Explanations of Emotions, Human Nature, 1, 26 1 -28 9, 1990 Trivers R., Social Evolution, Menlo Part, CA: Cummings, 1985 Webb, ... 17, 2, 28 5 -29 3 ,20 01 26 Koshizen, T and Rosseel, Y A New EM Algorithm using the Tikonov Regularizer and the GMB-REM Robot's Position Estimation System Int J of Knowle Intel Engi Syst., 5, 2- 14, 20 01 ... National Conference on Artificial Intelligence, Madison, Wisconsin, 721 - 726 , 20 01 Nash, J.F., Annals of Mathematics, 54 :28 6 -29 5, 1951 Kaneko, M and Matsui, A., Inductive Game Theory: Discrimination...
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