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Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Lecture 38 of 42
Wednesday, 03 December 2008
William H. Hsu
Department of Computing and Information Sciences, KSU
KSOL course page: http://snipurl.com/v9v3
Course web site: http://www.kddresearch.org/Courses/Fall-2008/CIS730
Instructor home page: http://www.cis.ksu.edu/~bhsu
Reading for Next Class:
Sections 22.1, 22.6-7, Russell & Norvig 2nd edition
Vision, Part 2 of 2Discussion: Machine Problem 7
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Vision OutlineVision 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
•http://www.cs.cmu.edu/~efros/courses/AP06/
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
So what do humans care about?
slide by Fei Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Verification: is that a bus?
slide by Fei Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Detection: are there cars?
slide by Fei Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Identification: is that a picture of Mao?
slide by Fei Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Object categorization
sky
building
flag
wallbanner
bus
cars
bus
face
street lamp
slide by Fei Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Scene and context categorization• outdoor
• city
• traffic
• …
slide by Fei Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Rough 3D layout, depth ordering
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 1: view point variation
Michelangelo 1475-1564
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 2: illumination
slide credit: S. Ullman
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 3: occlusion
Magritte, 1957
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 4: scale
slide by Fei Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 5: deformation
Xu, Beihong 1943
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 6: background clutter
Klimt, 1913
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 7: object intra-class variationChallenges 7: object intra-class variation
slide by Fei-Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 8: local ambiguityChallenges 8: local ambiguity
slide by Fei-Fei, Fergus & Torralba
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Challenges 9: the world behind the image Challenges 9: the world behind the image
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
In this course, we will:In this course, we will:
Take a few baby steps…
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Physiology of Vision: a swift overview
Physiology of Vision: a swift overview
16-721: Learning-Based Methods in VisionA. Efros, CMU, Spring 2007
Some figures from Steve Palmer
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Class IntroductionsClass Introductions
Name: Research area / project / advisor What you want to learn in this class? When I am not working, I ______________ Favorite fruit:
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Image FormationImage Formation
Digital Camera
The Eye
Film
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Monocular Visual Field: 160 deg (w) X 135 deg (h)Binocular Visual Field: 200 deg (w) X 135 deg (h)Monocular Visual Field: 160 deg (w) X 135 deg (h)Binocular Visual Field: 200 deg (w) X 135 deg (h)
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Point of observation
Figures © Stephen E. Palmer, 2002
What do we see?What do we see?
3D world 2D image
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Point of observation
What do we see?What do we see?
3D world 2D image
Painted backdrop
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
The Plenoptic FunctionThe Plenoptic Function
Q: What is the set of all things that we can ever see? A: The Plenoptic Function (Adelson & Bergen)
Let’s start with a stationary person and try to parameterize everything that he can see…
Figure by Leonard McMillan
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Grayscale snapshotGrayscale snapshot
is intensity of light Seen from a single view point At a single time Averaged over the wavelengths of the visible spectrum
(can also do P(x,y), but spherical coordinate are nicer)
P()
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Color snapshotColor snapshot
is intensity of light Seen from a single view point At a single time As a function of wavelength
P()
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Spherical PanoramaSpherical Panorama
All light rays through a point form a ponorama
Totally captured in a 2D array -- P() Where is the geometry???
See also: 2003 New Years Eve
http://www.panoramas.dk/fullscreen3/f1.html
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
A movieA movie
is intensity of light Seen from a single view point Over time As a function of wavelength
P(,t)
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Space-time imagesSpace-time images
x
y
t
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Holographic movieHolographic movie
is intensity of light Seen from ANY viewpoint Over time As a function of wavelength
P(,t,VX,VY,VZ)
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
The Plenoptic FunctionThe Plenoptic Function
Can reconstruct every possible view, at every moment, from every position, at every wavelength
Contains every photograph, every movie, everything that anyone has ever seen! it completely captures our visual reality! Not bad for a function…
P(,t,VX,VY,VZ)
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
The Eye is a cameraThe Eye is a camera
The human eye is a camera! Iris - colored annulus with radial muscles
Pupil - the hole (aperture) whose size is controlled by the iris What’s the “film”?
photoreceptor cells (rods and cones) in the retina
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
The RetinaThe Retina
Cross-section of eye
Ganglion cell layer
Bipolar cell layer
Receptor layer
Pigmentedepithelium
Ganglion axons
Cross section of retina
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Retina up-closeRetina up-close
Light
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence© Stephen E. Palmer, 2002
Cones cone-shaped less sensitive operate in high light color vision
Two types of light-sensitive receptors
cone
rod
Rods rod-shaped highly sensitive operate at night gray-scale vision
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Rod / Cone sensitivityRod / Cone sensitivity
The famous sock-matching problem…
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence© Stephen E. Palmer, 2002
Distribution of Rods and Cones.
0
150,000
100,000
50,000
020 40 60 8020406080
Visual Angle (degrees from fovea)
Rods
Cones Cones
Rods
FoveaBlindSpot
# R
ecep
tors
/mm
2
Night Sky: why are there more stars off-center?
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Electromagnetic SpectrumElectromagnetic Spectrum
http://www.yorku.ca/eye/photopik.htm
Human Luminance Sensitivity Function
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Why do we see light of these wavelengths?
© Stephen E. Palmer, 2002
.
0 1000 2000 3000
En
erg
y
Wavelength (nm)
400 700
700 C
2000 C
5000 C
10000 C
VisibleRegion
…because that’s where theSun radiates EM energy
Visible Light
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Retinal Processing
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Single Cell Recording
Microelectrode
Amplifier
Time
Electrical response(action potentials)
mV
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Single Cell Recording
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Retinal Receptive Fields
Receptive field structure in ganglion cells:On-center Off-surround
Stimulus condition Electrical response
Time
Response
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Receptive field structure in ganglion cells:On-center Off-surround
Stimulus condition Electrical response
Time
Response
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Receptive field structure in ganglion cells:On-center Off-surround
Stimulus condition Electrical response
Time
Response
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Receptive field structure in ganglion cells:On-center Off-surround
Stimulus condition Electrical response
Time
Response
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Receptive field structure in ganglion cells:On-center Off-surround
Stimulus condition Electrical response
Time
Response
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Receptive field structure in ganglion cells:On-center Off-surround
Stimulus condition Electrical response
Time
Response
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
RF of On-center Off-surround cells
Receptive FieldNeural Response
Center
Surround
On Off
Response Profile
on-center
off-surround
Horizontal Position
FiringRate
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
RF of Off-center On-surround cells
Receptive Field
Horizontal Position
on-surround
off-center
Response Profile
FiringRate
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Center
Surround
On Off
Neural Response
Surround
Center
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Retinal Receptive Fields
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Receptive field structure in bipolar cells
Light
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Receptive field structure in bipolar cells
Receptors
Bipolar Cell
A. WIRING DIAGRAM
HorizontalCells
Direct excitatory component (D)
B. RECEPTIVE FIELD PROFILES
LIGHT
Direct Path
Indirect Path
Indirectinhibitory
component (I)
D + I
Retinal Receptive Fields
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence© Stephen E. Palmer, 2002
Visual CortexVisual Cortex
aka:Primary visual cortexStriate cortexBrodman’s area 17
Cortical Area V1
ThalamusLGN
Striatecortex(V1)
DorsalStream
Parietalvisualcortex
Temporalvisualcortex
Eye Opticnerve
Extrastriatecortex
VentralStream
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Single-cell recording from visual cortex
David Hubel & Thorston Wiesel© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Single-cell recording from visual cortex
TimeTime
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Three classes of cells in V1
Simple cells
Complex cells
Hypercomplex cells
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Simple Cells: “Line Detectors”
A. Light Line Detector
Horizontal Position
FiringRate
B. Dark Line Detector
Horizontal Position
FiringRate
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Simple Cells: “Edge Detectors”
C. Dark-to-light Edge Detector
Horizontal Position
FiringRate
D. Light-to-dark Edge Detector
Horizontal Position
FiringRate
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Constructing a line detector
Receptive Fields
Retina LGN
Center-Surround Cells
Simple Cell
CorticalArea V1
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Complex Cells
STIMULUS NEURAL RESPONSE
Time
00o
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Complex Cells
STIMULUS NEURAL RESPONSE
Time
060o
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Complex Cells
STIMULUS NEURAL RESPONSE
Time
090o
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Complex Cells
STIMULUS NEURAL RESPONSE
Time
0120o
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Constructing a Complex Cell
Simple Cells
Cortical Area V1
Complex CellReceptive Fields
Retina
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Hypercomplex Cells
Time
STIMULUS NEURAL RESPONSE
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Hypercomplex Cells
Time
STIMULUS NEURAL RESPONSE
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Hypercomplex Cells
Time
STIMULUS NEURAL RESPONSE
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Hypercomplex Cells
Time
STIMULUS NEURAL RESPONSE
“End-stopped” Cells© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Time
STIMULUS NEURAL RESPONSE
“End-stopped” Simple Cells
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Cortical Receptive Fields
Constructing a Hypercomplex Cell
Receptive Fields
RETINA CORTICAL AREA V1
Complex Cell End-stopped Cell
© Stephen E. Palmer, 2002
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Mapping from Retina to V1Mapping from Retina to V1
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Why edges?Why edges?
So, why “edge-like” structures in the Plenoptic Function?
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Because our world is structured!Because our world is structured!
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Problem: Dynamic RangeProblem: Dynamic Range
15001500
11
25,00025,000
400,000400,000
2,000,000,0002,000,000,000
The real world isHigh dynamic range
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
pixel (312, 284) = 42pixel (312, 284) = 42
ImageImage
42 photos?42 photos?
Is Camera a photometer?Is Camera a photometer?
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Long ExposureLong Exposure
10-6 106
10-6 106
Real world
Picture
0 to 255
High dynamic range
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Short ExposureShort Exposure
10-6 106
10-6 106
Real world
Picture
0 to 255
High dynamic range
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Varying ExposureVarying Exposure
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
What does the eye sees?What does the eye sees?
The eye has a huge dynamic rangeDo we see a true radiance map?
Computing & Information SciencesKansas State University
Wednesday, 03 Dec 2008CIS 530 / 730: Artificial Intelligence
Eye is not a photometer!Eye is not a photometer!
"Every light is a shade, compared to the higher lights, till you come to the sun; and every shade is a light, compared to the deeper shades, till you come to the night."
— John Ruskin, 1879