776 Computer Vision

Jan-Michael Frahm & Enrique DunnSpring 2012

Photometric stereo (shape from shading)

• Can we reconstruct the shape of an object based on shading cues?

Luca della Robbia,Cantoria, 1438

Photometric stereo• Assume:

o A Lambertian objecto A local shading model (each point on a surface receives light only from

sources visible at that point)o A set of known light source directionso A set of pictures of an object, obtained in exactly the same

camera/object configuration but using different sourceso Orthographic projection

• Goal: reconstruct object shape and albedo

Sn

???S1

S2

Forsyth & Ponce, Sec. 5.4

slide: S. Lazebnik

Surface model: Monge patch

Forsyth & Ponce, Sec. 5.4

j

j

j

j

Vyxg

SkyxNyx

SyxNyxk

yxBkyxI

),(

)(,,

,,

),(),(

Image model• Known: source vectors Sj and pixel values Ij(x,y)• We also assume that the response function of the

camera is a linear scaling by a factor of k • Combine the unknown normal N(x,y) and albedo

ρ(x,y) into one vector g, and the scaling constant k and source vectors Sj into another vector Vj:

slide: S. Lazebnik

Least squares problem

• Obtain least-squares solution for g(x,y)• Since N(x,y) is the unit normal, (x,y) is given by

the magnitude of g(x,y) (and it should be less than 1)

• Finally, N(x,y) = g(x,y) / (x,y)

),(

),(

),(),(

2

1

2

1

yxg

V

VV

yxI

yxIyxI

Tn

T

T

n

(n × 1)known known unknown

(n × 3) (3 × 1)

• For each pixel, we obtain a linear system:

slide: S. Lazebnik

Example

Recovered albedo Recovered normal field

Forsyth & Ponce, Sec. 5.4

•Recall the surface is written as

•This means the normal has the form:

Recovering a surface from normals

•If we write the estimated vector g as

•Then we obtain values for the partial derivatives of the surface:

(x,y, f (x, y))

g(x,y) g1(x, y)g2 (x, y)g3(x, y)

fx (x,y) g1(x, y) g3(x, y)

fy(x, y) g2(x, y) g3(x,y)

N(x,y) 1

fx2 fy

2 1

fx

fy

1

slide: S. Lazebnik

Recovering a surface from normals

•Integrability: for the surface f to exist, the mixed second partial derivatives must be equal:

•We can now recover the surface height at any point by integration along some path, e.g.

g1(x, y) g3(x, y) y

g2(x, y) g3(x, y) x

f (x, y) fx (s, y)ds0

x

fy (x, t)dt0

y

c

(for robustness, can take integrals over many different paths and average the results)

(in practice, they should at least be similar)

slide: S. Lazebnik

Surface recovered by integration

Forsyth & Ponce, Sec. 5.4

Reading• Szeliski 2.2-2.3• Szeliski 3.1-3.2

Typical image operations

image: R. Szeliski

Color

Phillip Otto Runge (1777-1810)

What is color?• Color is the result of interaction

between physical light in the environment and our visual system

• Color is a psychological property of our visual experiences when we look at objects and lights, not a physical property of those objects or lights (S. Palmer, Vision Science: Photons to Phenomenology)

slide: S. Lazebnik

Electromagnetic spectrum

Human Luminance Sensitivity Function

Interaction of light and surfaces

• Reflected color is the result of interaction of light source spectrum with surface reflectance

slide: S. Lazebnik

Spectra of some real-world surfaces

metamers

image: W. Freeman

Standardizing color experience

• We would like to understand which spectra produce the same color sensation in people under similar viewing conditions

• Color matching experiments

Foundations of Vision, by Brian Wandell, Sinauer Assoc., 1995

Color matching experiment 1

Source: W. Freeman

Color matching experiment 1

p1 p2 p3

Source: W. Freeman

Color matching experiment 1

p1 p2 p3

Source: W. Freeman

Color matching experiment 1

p1 p2 p3

The primary color amounts needed for a match

Source: W. Freeman

Color matching experiment 2

Source: W. Freeman

Color matching experiment 2

p1 p2 p3

Source: W. Freeman

Color matching experiment 2

p1 p2 p3

Source: W. Freeman

Color matching experiment 2

p1 p2 p3 p1 p2 p3

We say a “negative” amount of p2 was needed to make the match, because we added it to the test color’s side.

The primary color amounts needed for a match:

p1 p2 p3

Source: W. Freeman

Trichromacy• In color matching experiments, most people can

match any given light with three primarieso Primaries must be independent

• For the same light and same primaries, most people select the same weightso Exception: color blindness

• Trichromatic color theoryo Three numbers seem to be sufficient for encoding coloro Dates back to 18th century (Thomas Young)

slide: S. Lazebnik

Grassman’s Laws• Color matching appears to be linear• If two test lights can be matched with the same set

of weights, then they match each other: o Suppose A = u1 P1 + u2 P2 + u3 P3 and B = u1 P1 + u2 P2 + u3 P3. Then A =

B.• If we mix two test lights, then mixing the matches

will match the result:o Suppose A = u1 P1 + u2 P2 + u3 P3 and B = v1 P1 + v2 P2 + v3 P3. Then A + B

= (u1+v1) P1 + (u2+v2) P2 + (u3+v3) P3.• If we scale the test light, then the matches get

scaled by the same amount:o Suppose A = u1 P1 + u2 P2 + u3 P3.

Then kA = (ku1) P1 + (ku2) P2 + (ku3) P3.

slide: S. Lazebnik

Linear color spaces• Defined by a choice of three primaries • The coordinates of a color are given by the

weights of the primaries used to match it

mixing two lights producescolors that lie along a straight

line in color space

mixing three lights produces colors that lie within the triangle

they define in color space

slide: S. Lazebnik

How to compute the weights of the primaries

to match any spectral signal

• Matching functions: the amount of each primary needed to match a monochromatic light source at each wavelength

p1 p2 p3

?Given: a choice of three primaries and a target color signal

Find: weights of the primaries needed to match the color signal

p1 p2 p3

slide: S. Lazebnik

RGB space• Primaries are monochromatic lights (for monitors,

they correspond to the three types of phosphors)• Subtractive matching required for some wavelengths

RGB matching functionsRGB primaries

slide: S. Lazebnik

How to compute the weights of the primaries

to match any spectral signal• Let c(λ) be one of the matching functions,

and let t(λ) be the spectrum of the signal. Then the weight of the corresponding primary needed to match t is

dtcw )()(

λ

Matching functions, c(λ)

Signal to be matched, t(λ)

slide: S. Lazebnik

Nonlinear color spaces: HSV

• Perceptually meaningful dimensions: Hue, Saturation, Value (Intensity)

• RGB cube on its vertex

slide: S. Lazebnik

Color perception• Color/lightness constancy

o The ability of the human visual system to perceive the intrinsic reflectance properties of the surfaces despite changes in illumination conditions

• Instantaneous effectso Simultaneous contrasto Mach bands

• Gradual effectso Light/dark adaptationo Chromatic adaptationo Afterimages

J. S. Sargent, The Daughters of Edward D. Boit, 1882 slide: S. Lazebnik

Simultaneous contrast/Mach bands

Source: D. Forsyth

Chromatic adaptation• The visual system changes its sensitivity

depending on the luminances prevailing in the visual fieldo The exact mechanism is poorly understood

• Adapting to different brightness levelso Changing the size of the iris opening (i.e., the aperture) changes

the amount of light that can enter the eye o Think of walking into a building from full sunshine

• Adapting to different color temperatureo The receptive cells on the retina change their sensitivity o For example: if there is an increased amount of red light, the cells

receptive to red decrease their sensitivity until the scene looks white again

o We actually adapt better in brighter scenes: This is why candlelit scenes still look yellow

http://www.schorsch.com/kbase/glossary/adaptation.htmlslide: S. Lazebnik

White balance• When looking at a picture on screen or print, we

adapt to the illuminant of the room, not to that of the scene in the picture

• When the white balance is not correct, the picture will have an unnatural color “cast”

http://www.cambridgeincolour.com/tutorials/white-balance.htm

incorrect white balance

correct white balance

slide: S. Lazebnik

White balance• Film cameras:

o Different types of film or different filters for different illumination conditions

• Digital cameras: o Automatic white balanceo White balance settings corresponding to

several common illuminantso Custom white balance using a reference

object

http://www.cambridgeincolour.com/tutorials/white-balance.htmslide: S. Lazebnik

White balance• Von Kries adaptation

o Multiply each channel by a gain factor

slide: S. Lazebnik

White balance• Von Kries adaptation

o Multiply each channel by a gain factor

• Best way: gray cardo Take a picture of a neutral object (white or gray)o Deduce the weight of each channel

• If the object is recoded as rw, gw, bw use weights 1/rw, 1/gw, 1/bw

slide: S. Lazebnik

White balance• Without gray cards: we need to “guess” which

pixels correspond to white objects• Gray world assumption

o The image average rave, gave, bave is grayo Use weights 1/rave, 1/gave, 1/bave

• Brightest pixel assumptiono Highlights usually have the color of the light source o Use weights inversely proportional to the values of the brightest

pixels• Gamut mapping

o Gamut: convex hull of all pixel colors in an imageo Find the transformation that matches the gamut of the image to

the gamut of a “typical” image under white light• Use image statistics, learning techniques

slide: S. Lazebnik

White balance by recognition

• Key idea: For each of the semantic classes present in the image, compute the illuminant that transforms the pixels assigned to that class so that the average color of that class matches the average color of the same class in a database of “typical” images

J. Van de Weijer, C. Schmid and J. Verbeek, Using High-Level Visual Information for Color Constancy, ICCV 2007.slide: S. Lazebnik

Mixed illumination• When there are several types of illuminants in the

scene, different reference points will yield different results

http://www.cambridgeincolour.com/tutorials/white-balance.htm

Reference: moon Reference: stone

slide: S. Lazebnik

Spatially varying white balance

E. Hsu, T. Mertens, S. Paris, S. Avidan, and F. Durand, “Light Mixture Estimation for Spatially Varying White Balance,” SIGGRAPH 2008

Input Alpha map Output

slide: S. Lazebnik

Uses of color in computer vision

Color histograms for image matching

http://labs.ideeinc.com/multicolr slide: S. Lazebnik

Uses of color in computer vision

Image segmentation and retrieval

C. Carson, S. Belongie, H. Greenspan, and Ji. Malik, Blobworld: Image segmentation using Expectation-Maximization and its application to image querying, ICVIS 1999.

slide: S. Lazebnik

Uses of color in computer vision

Skin detection

M. Jones and J. Rehg, Statistical Color Models with Application to Skin Detection, IJCV 2002. slide: S. Lazebnik

Uses of color in computer vision

Robot soccer

M. Sridharan and P. Stone, Towards Eliminating Manual Color Calibration at RoboCup. RoboCup-2005: Robot Soccer World Cup IX, Springer Verlag, 2006

Source: K. Grauman

Uses of color in computer vision

Building appearance models for tracking

D. Ramanan, D. Forsyth, and A. Zisserman. Tracking People by Learning their Appearance. PAMI 2007. slide: S. Lazebnik

Linear filtering

slide: S. Lazebnik

Motivation: Image denoising• How can we reduce noise in a photograph?

slide: S. Lazebnik

• Let’s replace each pixel with a weighted average of its neighborhood

• The weights are called the filter kernel• What are the weights for the average of a

3x3 neighborhood?

Moving average

111

111

111

“box filter”

Source: D. Lowe

Defining convolution

lk

lkglnkmfnmgf,

],[],[],)[(

f

• Let f be the image and g be the kernel. The output of convolving f with g is denoted f * g.

Source: F. Durand

• MATLAB functions: conv2, filter2, imfilter

Convention: kernel is “flipped”

Key properties• Linearity: filter(f1 + f2) = filter(f1) + filter(f2)• Shift invariance: same behavior regardless of

pixel location: filter(shift(f)) = shift(filter(f))• Theoretical result: any linear shift-invariant

operator can be represented as a convolution

slide: S. Lazebnik

Properties in more detail• Commutative: a * b = b * a

o Conceptually no difference between filter and signal• Associative: a * (b * c) = (a * b) * c

o Often apply several filters one after another: (((a * b1) * b2) * b3)o This is equivalent to applying one filter: a * (b1 * b2 * b3)

• Distributes over addition: a * (b + c) = (a * b) + (a * c)• Scalars factor out: ka * b = a * kb = k (a * b)• Identity: unit impulse e = […, 0, 0, 1, 0, 0, …],

a * e = a

slide: S. Lazebnik

Annoying details• What is the size of the output?• MATLAB: filter2(g, f, shape)

o shape = ‘full’: output size is sum of sizes of f and go shape = ‘same’: output size is same as fo shape = ‘valid’: output size is difference of sizes of f and g

f

gg

gg

f

gg

gg

f

gg

gg

full same valid

slide: S. Lazebnik

Annoying details• What about near the edge?

o the filter window falls off the edge of the imageo need to extrapolateo methods:

• clip filter (black)• wrap around• copy edge• reflect across edge

Source: S. Marschner

Annoying details• What about near the edge?

o the filter window falls off the edge of the imageo need to extrapolateo methods (MATLAB):

• clip filter (black): imfilter(f, g, 0)• wrap around: imfilter(f, g, ‘circular’)• copy edge: imfilter(f, g, ‘replicate’)• reflect across edge: imfilter(f, g, ‘symmetric’)

Source: S. Marschner

Practice with linear filters

000

010

000

Original

?

Source: D. Lowe

Practice with linear filters

000

010

000

Original Filtered (no change)

Source: D. Lowe

Practice with linear filters

000

100

000

Original

?

Source: D. Lowe

Practice with linear filters

000

100

000

Original Shifted leftBy 1 pixel

Source: D. Lowe

Practice with linear filters

Original

?111

111

111

Source: D. Lowe

Practice with linear filters

Original

111

111

111

Blur (with abox filter)

Source: D. Lowe

Practice with linear filters

Original

111111111

000020000 - ?

(Note that filter sums to 1)

Source: D. Lowe

Practice with linear filters

Original

111111111

000020000 -

Sharpening filter- Accentuates differences with

local average

Source: D. Lowe

Sharpening

Source: D. Lowe

Sharpening with unsharp masking

• What does blurring take away?

original smoothed (5x5)

–

detail

=

sharpened

=

Let’s add it back:

original detail

+

slide: S. Lazebnik