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

Today’s Goals Learn about CS223b Get Excited about Computer Vision Learn about Image Formation (tbc)

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

People Involved You! (63 students) Me! Rick Szeliski, Microsoft Hendrik Dahlkamp:

The Text

Course Overview Basics 3D Reconstruction Motion 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

Course Outline http://cs223b.stanford.edu/schedule.html

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!

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!

Grading Criteria 10% Participation 30% Assignments 30% Midterm exam 30% Project (35% of all students received an A in CS223b-04)

Today’s Goals Learn about CS223b Get Excited about Computer Vision Learn about image formation (tbc)

Computer Graphics Output Image Model Synthetic Camera (slides courtesy of Michael Cohen)

Computer Vision Output Model Real Scene Real Cameras (slides courtesy of Michael Cohen)

Combined Output Image Model Real Scene Synthetic Camera Real Cameras (slides courtesy of Michael Cohen)

Example 1:Stereo See http://schwehr.org/photoRealVR/example.html

Example 2: Structure From Motion http://medic.rad.jhmi.edu/pbazin/perso/Research/SfMvideo.html

Example 3: 3D Modeling http://www.photogrammetry.ethz.ch/research/cause/3dreconstruction3.html

Example 4: Classification http://elib.cs.berkeley.edu/photos/classify/

Example 4: Classification http://elib.cs.berkeley.edu/photos/classify/

Example 5: Detection and Tracking http://www.seeingmachines.com/facelab.htm

Example 6: Optical Flow David Stavens, Andrew Lookingbill, David Lieb, CS223b Winter 2004

Example 7: Learning Demo: Dirt Road Andrew Lookingbill, David Lieb, CS223b Winter 2004

Example 8: Human Vision

Example 8: Human Vision

Excited Yet?

Computer Vision [Trucco&Verri’98]

Today’s Goals Learn about CS223b Get Excited about Computer Vision Learn about image formation (tbc)

Topics Pinhole Camera Orthographic Projection Perspective Camera Model Weak-Perspective Camera Model

Pinhole Camera -- Brunelleschi, XVth Century “shards of diamond” – those little pieces of beautiful truth scattered about. -- Brunelleschi, XVth Century *many slides in this lecture from Marc Pollefeys comp256, Lect 2

Perspective Projection Derive the perspective equations on notes page 1.1 A “similar triangle’s” approach to vision. Notes 1.1 Marc Pollefeys

Perspective Projection X x -x Z f

Consequences: Parallel lines meet There exist vanishing points Marc Pollefeys

Vanishing points Different directions correspond VPL VPR VP1 VP2 Different directions correspond to different vanishing points VP3 Marc Pollefeys

The Effect of Perspective

Implications For Perception* Same size things get smaller, we hardly notice… Parallel lines meet at a point… * A Cartoon Epistemology: http://cns-alumni.bu.edu/~slehar/cartoonepist/cartoonepist.html

Perspective Projection X -x Z f

Weak Perspective Projection Z O -x Z Z f

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

Pictorial Comparison Weak perspective Perspective  Marc Pollefeys

Summary: Perspective Laws Weak perspective Orthographic

Limits for pinhole cameras