Themes in Computer Vision

Carlo Tomasi

Applications

• autonomous cars, planes, missiles, robots, ...• space exploration• aid to the blind, ASL recognition• manufacturing,

quality control• surveillance, security• image retrieval• medical imaging• ...• perceptual input for

cognition

(CMU NavLab ‘90)

Vision is Effortless to Us

• driving a car

• threading a needle

• recognizing a distant, occluded object

• understanding (flat!) pictures

• perceive the mood of a painting

Technical Difficulties

• 512x512x3x30 ≈ 23.5MB/s was a problem 10 years ago

• technology just gotgood enough

• great opportunity!

Fundamental Challenges I

• 3D2D implies information loss

• sensitivity to errors

• need for models

graphics

vision

Reconstruction and Geometry

must use redundancy toaddress sensitivity to noise

Reconstruction Example

(Tomasi & Kanade ‘91)

Fundamental Challenges II

• Appearance changes with viewpoint, i.e., the same thing looks different• Geometric changes: surface slant depends on

viewpoint• Photometric changes: surface brightness and

color depend on viewpoint• Occlusions: what is hidden depends on

viewpoint

• Ambiguity: different things look similar• Correspondence is hard

Photometric and Geometric Change

Occlusion

?

Technicality: Motion Blur

Wrong Correspondence

Simple Images are Harder

(Birchfield and Tomasi ‘01)

Models

• must be insensitive to• viewing position

changes• lighting changes• object configuration

changes• occlusion• clutter

• must be sensitive to• object changes!

Low-Level Models are General

Model: surfaces are smooth, connected

(Marr and Poggio ‘80)

Higher-Level Models Work Better…

•… when they are right• (and much worse when they are wrong)

(Lin and Tomasi ‘01)

State of the Artle

ft in

put i

mag

eground truth disparity

our

resu

ltdisparity error

(Lin and Tomasi, 01)

Fundamental Challenges III• An old problem in the

new context of recognition:• Variation of appearance:

Objects change over time, with context, viewpoint, lighting, pose, expression,…

• Similarity: Different objects look similar

• [BTW, objects do not always appear in isolation…]

(US Army FERET Database)

Modeling Images as Points12

n

...

...

1

2

n

principal componentsform an approximate basisfor all the images in the set

... ... ... ... ... ... ... ...

Example: Eigenfaces

(Turk, Pentland ‘91; Murase-Nayar ‘93; many others)

........................

...

=

the projection of a new imageonto the eigenbasis isa compressed representationof that image

can use this to recognize faces,synthesize new images, ...

Fundamental Challenges IV:

“read my lips”

“run”• Variation, self-occlusion,occlusion, clutter, …

Motions can be complex

Simple Models Are Fast

(Birchfield ‘98)

a head is an ellipse with two colors,surrounded by strong intensity gradients

(Bregler ‘93)

2D Articulated Models for Tracking

3D Models are More Accurate…

•… when they are right• [BTW, why is she wearing a black shirt?]

(Isard & Blake ‘99)

Probabilistic Models Handle Uncertainty

• world state , observation (image) • prior P()

• colors change moderately (?)• arms move with limited acceleration (boxing?)• the height of a head can only change so much (dancing?)• contours are smooth and change smoothly• balls follow the laws of gravity• …

• sensor model P(|)• image motion can be measured only so well• motion blurs the image• noise corrupts pixel values• ...

Bayesian Tracking

• Bayes’ rule: P(|) P(|) P()

• what is the world state likely to be, given that we observed the image ?

(Isard & Blake ‘99)

Even Higher Models May Be Needed

[MY COMPUTER CAN UNDERSTAND SIGN] computer No(1(HandsIpsi 1 1 0 S Out Down, NeutralIpsi 0 0 0 S Out Down)( ,-) 0(" " 0 -1 " " ", " " " " " " ") (",-) 0(" " -1 0 " " ", " " " " " " ") (",-) 0(" " 0 1 " " ", " " " " " " ") (",-) 1(" " 1 0 " " ", " " " " " " ")) understand No(1(HandIn 0 0 0 X Out Contra,NeutralOut 0 0 0 D Up Contra)(-,-) "(" 1 " " " " ", " " " " " " "))signs No(1( 0 0 0 B Up Out, - - - - - - -) (-,-) "(" 1 0 0 " " ", - - - - - - -))can No(1(HandUp 0 0 0 Out Contra,NeutralOut 0 0 -1 B Out Up) (-,-) "(" " " " " " ", " " " 1 " " "))

(Richards & Tomasi ‘02)

Fundamental Challenge V:Images are Diverse

Previous Work in Image Retrieval

Hulton Deutsch

Color and Texture Models

orientation

scal

e

text

ure

Image Distances

(Rubner & Tomasi ‘97)

(Rubner & Tomasi ‘97)

Retrieval by Refinement - 1

(Rubner & Tomasi ‘97)

Retrieval by Refinement - 2

(Rubner & Tomasi ‘97)

Vision is AI Complete

• Vision is an inverse problem

• Strong models of the world are required

• Vision implies reasoning about the world

• Vision is AI