16-721: Advanced Machine Perception Staff: Instructor: Alexei (Alyosha) Efros 4207 TA: David Bradley 2216 NSH Web Page: urses/AP06/ urses/AP06/

Today Introduction Why Perception? Administrative stuff Overview of the course Image Datasets

A bit about me Alexei (Alyosha) Efros Relatively new faculty (RI/CSD) Ph.D 2003, from UC Berkeley (signed by Arnie!) Research Fellow, University of Oxford, ’03-’04 Teaching I am still learning… The plan is to have fun and learn cool things, both you and me! Social warning: I don’t see well Research Vision, Graphics, Data-driven “stuff”

PhD Thesis on Texture and Action Synthesis Antonio Criminisi’s son cannot walk but he can fly Smart Erase button in Microsoft Digital Image Pro:

The story begins… “All happy families are alike; each unhappy family is unhappy in its own way.” -- Lev Tolstoy, Anna Karenina “What does it mean, to see? The plain man's answer (and Aristotle's, too). would be, to know what is where by looking.” -- David Marr, Vision (1982)

Vision: a split personality “What does it mean, to see? The plain man's answer (and Aristotle's, too). would be, to know what is where by looking. In other words, vision is the process of discovering from images what is present in the world, and where it is.” Answer #1: pixel of brightness 243 at position (124,54) …and depth.7 meters Answer #2: looks like bottom edge of whiteboard showing at the top of the image Is the difference just a matter of scale? depth map

Measurement vs. Perception

Brightness: Measurement vs. Perception

Proof!

Lengths: Measurement vs. Perception Müller-Lyer Illusion

Vision as Measurement Device Real-time stereo on Mars Structure from Motion Physics-based Vision Virtualized Reality

…but why? Reason #1: Semester too short, can’t cover everything Other great classes offered at CMU, e.g.: –Appearance Modeling (Srinivas Narasimhan, every fall) –Medical Vision (Yanxi Liu) –Structure from Motion (Martial Hebert, sometime?) “But what if I don’t care about this wishy-washy human perception stuff? I just want to make my robot go!” Reason #2: For measurement, other sensors are often better (in DARPA Grand Challenge, vision was barely used!) Reason #3: The goals of computer vision (what + where) are in terms of what humans care about.

So what do humans care about? slide by Fei Fei, Fergus & Torralba

Verification: is that a bus? slide by Fei Fei, Fergus & Torralba

Detection: are there cars? slide by Fei Fei, Fergus & Torralba

Identification: is that a picture of Mao? slide by Fei Fei, Fergus & Torralba

Object categorization sky building flag wall banner bus cars bus face street lamp slide by Fei Fei, Fergus & Torralba

Scene and context categorization outdoor city traffic … slide by Fei Fei, Fergus & Torralba

Rough 3D layout, depth ordering

Challenges 1: view point variation Michelangelo

Challenges 2: illumination slide credit: S. Ullman

Challenges 3: occlusion Magritte, 1957

Challenges 4: scale slide by Fei Fei, Fergus & Torralba

Challenges 5: deformation Xu, Beihong 1943

Challenges 6: background clutter Klimt, 1913

Challenges 7: object intra-class variation slide by Fei-Fei, Fergus & Torralba

Challenges 8: local ambiguity slide by Fei-Fei, Fergus & Torralba

Challenges 9: the world behind the image

In this course, we will: Take a few baby steps…

Course Organization Requirements: 1.Paper Presentations (50%) Paper Advocate Paper Demo Presenter Paper Opponent 2.Class Participation (20%) Keep annotated bibliography Post questions / comments on Quick-topic Ask questions / debate / flight / be involved! 3.Final Project (30%) Do something with lots of data (at least 500 images) Groups of 1, 2, or 3

Paper Advocate 1.Pick a paper from list That you like and willing to defend Sometimes I will make you do two papers, or background 2.Meet with me before starting to talk about how to present the paper(s) 3.Prepare a good, conference-quality presentation (20-45 min, depending on difficulty of material) 4.Meet with me again 2 days before class to go over the presentation Office hours at end of each class 5.Present and defend the paper in front of class

Paper Demo Presenter For some papers, we will have separate demo presentations 1.Sign up for a paper you find interesting 2.Get the code online (or implement if easy) 3.Run it on a toy problem, play with parameters 4.Run it on a new dataset 5.Prepare short 5-10 min presentation detailing results 6.Can cooperate with Paper Advocate

Paper Opponent 1.Sign up for a paper you don’t like / suspicious about 2.Prepare an argument (with or without slides) against the paper: Paper weaknesses Relevance to real problems Existence of better alternative approaches Etc. 3.Present in front of class (5-10 min)

Class Participation Keep annotated bibliography of papers you read (always a good idea!). The format is up to you. At least, it needs to have: Summary of key points A few Interesting insights, “aha moments”, keen observations, etc. Weaknesses of approach. Unanswered questions. Areas of further investigation, improvement. Submit your thoughts for current paper(s) before each class (printout)

Class Participation In addition, submit interesting observations or questions to QuickTopic before class for public discussion. Be active in class. Voice your ideas, concerns. You need to participate: either in class or in QuickTopic every week! Dave will be watching and keeping track!

Final Project Can grow out of paper presentation, or your own research But it needs to use large amounts of data! 1-3 people per project. Project proposals in a few weeks. Project presentations at the end of semester. Results presented as a CVPR-format paper. Hopefully, a few papers may be submitted to conferences.

End of Semester Awards We will vote for: Best Paper Presenter Best Paper Opponent Best Demo Best Project Prize: dinner in a nice restaurant

Course Outline Physiology of Vision (1 lecture) Overview of Human Visual Percetion (1 lecture) Need presenter for Monday! Part I: Low-level vision (images as texture) Texture segmentation, image retrieval, scene models, “Bag of words” representations Part II: Mid-level vision (segmentation) Principles of grouping, Normalized Cuts, Mean-shift, DD- MCMC, Graph-cut, super-pixels Part III: 2D Recognition Window scanning (Schniderman+Kanade, Viola+Jones) Correspondence Matching (schanfer matching, housedorf distance, shape contexts, invariant features, active appearance models) Recognition with Segmentation (top-down + buttom-up) Words and Pictures

Course Outline (cont.) Part IV: Intrinsic Images Shading vs. reflectance Recovering surface orientations and depth Style vs. content Part V: Dealing with Data Isomap, LLE, Non-negative Matrix Factorization Part VI: Tracking and Motion Segmentation Particle filtering, examplar-based, layers Sign up to present one paper on Wed on QuickTopic

Datasets See web page