Sebastian Thrun CS223B Computer Vision, Winter Stanford CS223B Computer Vision, Winter 2005 Lecture 1 Intro and Image Formation Sebastian Thrun, Stanford Rick Szeliski, Microsoft Hendrik Dahlkamp, Stanford

Sebastian Thrun CS223B Computer Vision, Winter Today’s Goals Learn about CS223b Get Excited about Computer Vision Learn about Image Formation (tbc)

Sebastian Thrun CS223B Computer Vision, Winter Administrativa Time and Location Tue/Thu 1:15-2:35, Gates B03 SCPD Televised (Live on Channel E5) Web site Class list (announcements only) Class newsgroup (discussion) su.class.cs223b (server: news.stanford.edu)

Sebastian Thrun CS223B Computer Vision, Winter People Involved You! (63 students) Me! Rick Szeliski, Microsoft Hendrik Dahlkamp:

Sebastian Thrun CS223B Computer Vision, Winter

6 The Text

Sebastian Thrun CS223B Computer Vision, Winter Course Overview Basics –Image Formation and Camera Calibration –Image Features 3D Reconstruction –Stereo –Image Mosaics Motion –Optical Flow –Structure From Motion –Tracking Object detection and recognition –Grouping –Detection –Segmentaiton –Classification

Sebastian Thrun CS223B Computer Vision, Winter Course Outline

Sebastian Thrun CS223B Computer Vision, Winter Goals To familiarize you with basic the techniques and jargon in the field To enable you to solve computer vision problems To let you experience (and appreciate!) the difficulties of real-world computer vision To get you excited!

Sebastian Thrun CS223B Computer Vision, Winter Requirements Attend + participate in all classes except at most two Turn in all assignments (even if for zero credit) Pass the midterm exam Successfully carry out research project –Jan 31: selection –Feb 14: Interim report –March 8/10: Class presentation –March 15: Final report No exceptions!

Sebastian Thrun CS223B Computer Vision, Winter Grading Criteria 10% Participation 30% Assignments 30% Midterm exam 30% Project (35% of all students received an A in CS223b-04)

Sebastian Thrun CS223B Computer Vision, Winter Today’s Goals Learn about CS223b Get Excited about Computer Vision Learn about image formation (tbc)

Sebastian Thrun CS223B Computer Vision, Winter Computer Graphics Image Output Model Synthetic Camera (slides courtesy of Michael Cohen)

Sebastian Thrun CS223B Computer Vision, Winter Real Scene Computer Vision Real Cameras Model Output (slides courtesy of Michael Cohen)

Sebastian Thrun CS223B Computer Vision, Winter Combined Model Real Scene Real Cameras Image Output Synthetic Camera (slides courtesy of Michael Cohen)

Sebastian Thrun CS223B Computer Vision, Winter Example 1:Stereo See

Sebastian Thrun CS223B Computer Vision, Winter Example 2: Structure From Motion

Sebastian Thrun CS223B Computer Vision, Winter Example 3: 3D Modeling

Sebastian Thrun CS223B Computer Vision, Winter Example 4: Classification

Sebastian Thrun CS223B Computer Vision, Winter Example 4: Classification

Sebastian Thrun CS223B Computer Vision, Winter Example 5: Detection and Tracking

Sebastian Thrun CS223B Computer Vision, Winter Example 6: Optical Flow David Stavens, Andrew Lookingbill, David Lieb, CS223b Winter 2004

Sebastian Thrun CS223B Computer Vision, Winter Example 7: Learning Andrew Lookingbill, David Lieb, CS223b Winter 2004 Demo: Dirt Road

Sebastian Thrun CS223B Computer Vision, Winter Example 8: Human Vision

Sebastian Thrun CS223B Computer Vision, Winter Example 8: Human Vision

Sebastian Thrun CS223B Computer Vision, Winter Excited Yet?

Sebastian Thrun CS223B Computer Vision, Winter Computer Vision [ Trucco&Verri’98]

Sebastian Thrun CS223B Computer Vision, Winter Today’s Goals Learn about CS223b Get Excited about Computer Vision Learn about image formation (tbc)

Sebastian Thrun CS223B Computer Vision, Winter Topics Pinhole Camera Orthographic Projection Perspective Camera Model Weak-Perspective Camera Model

Sebastian Thrun CS223B Computer Vision, Winter Pinhole Camera *many slides in this lecture from Marc Pollefeys comp256, Lect 2 -- Brunelleschi, XVth Century

Sebastian Thrun CS223B Computer Vision, Winter Perspective Projection A “similar triangle’s” approach to vision. Notes 1.1 Marc Pollefeys

Sebastian Thrun CS223B Computer Vision, Winter Perspective Projection x fZ X O -x

Sebastian Thrun CS223B Computer Vision, Winter Consequences: Parallel lines meet There exist vanishing points Marc Pollefeys

Sebastian Thrun CS223B Computer Vision, Winter Vanishing points VPL VPR H VP 1 VP 2 VP 3 Different directions correspond to different vanishing points Marc Pollefeys

Sebastian Thrun CS223B Computer Vision, Winter The Effect of Perspective

Sebastian Thrun CS223B Computer Vision, Winter Implications For Perception* * A Cartoon Epistemology: Same size things get smaller, we hardly notice… Parallel lines meet at a point…

Sebastian Thrun CS223B Computer Vision, Winter Perspective Projection fZ X O -x

Sebastian Thrun CS223B Computer Vision, Winter Weak Perspective Projection f Z O -x Z Z

Sebastian Thrun CS223B Computer Vision, Winter Generalization of Orthographic Projection When the camera is at a (roughly constant) distance from the scene, take m=1. Marc Pollefeys

Sebastian Thrun CS223B Computer Vision, Winter Pictorial Comparison  Weak perspective Perspective Marc Pollefeys

Sebastian Thrun CS223B Computer Vision, Winter Summary: Perspective Laws 1.Perspective 2.Weak perspective 3.Orthographic

Sebastian Thrun CS223B Computer Vision, Winter Limits for pinhole cameras