Lecture 1 - Stanford Vision Lab; Prof. Fei-Fei Li

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Fei-Fei Li! Shape and ligh.ng cues: Shading Lecture 1 - ! 43 24-‐Sep-‐12 Source: J. Koenderink
Page 1 and 2: Fei-Fei Li! Lecture 1: Introduc.on
Page 3 and 4: Fei-Fei Li! Today’s agenda • In
Page 5 and 6: Fei-Fei Li! What about this? Lectur
Page 7 and 8: Engineering Fei-Fei Li! What is it
Page 9 and 10: What is (computer) vision? Image (o
Page 11 and 12: Human vision is superbly efficient
Page 13 and 14: Fei-Fei Li! 150 ms !! Lecture 1 - !
Page 15 and 16: Fei-Fei Li! Change blindess Lecture
Page 17 and 18: Fei-Fei Li! Percep.on Lecture 1 - !
Page 19 and 20: Fei-Fei Li! Lecture 1 - ! 19 24-
Page 21 and 22: Fei-Fei Li! The goal of computer vi
Page 23 and 24: Fei-Fei Li! What kind of informa.on
Page 25 and 26: Vision as a source of semantic info
Page 27 and 28: Fei-Fei Li! Why study computer visi
Page 29 and 30: Challenges: viewpoint variation Mic
Page 31 and 32: Challenges: scale Fei-Fei Li! Lectu
Page 33 and 34: Challenges: occlusion Fei-Fei Li! M
Page 35 and 36: Challenges: Motion Fei-Fei Li! Lect
Page 37 and 38: Fei-Fei Li! Challenges: local ambig
Page 39 and 40: Fei-Fei Li! Depth cues: Linear pers
Page 41: Fei-Fei Li! Depth ordering cues: Oc
Page 45 and 46: Grouping cues: Similarity (color, t
Page 47 and 48: Fei-Fei Li! Bogom line • Percep.o
Page 49 and 50: Computer Vision in the Real World F
Page 51 and 52: Fei-Fei Li! 3D urban modeling Bing
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Page 62 and 63: Fei-Fei Li! Automo.ve safety • Mo
Page 64 and 65: Vision-‐based interac.on (and g
Page 66 and 67: Fei-Fei Li! The computer vision ind
Page 68 and 69: Fei-Fei Li! Overall philosophy •
Page 70 and 71: • Go to website… Fei-Fei Li! Sy
Page 72 and 73: Fei-Fei Li! Course Project: overvie
Page 74 and 75: • Original research ideas encoura
Page 76: Fei-Fei Li! Grading policy • Cour

Lecture 1 - Stanford Vision Lab; Prof. Fei-Fei Li

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