Object recognition under varying illumination

Lighting changes objects appearance.

SpecularLambertian

How do we recognize these objects?

Few Definitions: Reflection

• Reflection - The scattering of light from an object.

• Two extreme cases: diffuse reflection and specular reflection.

• Real objects reflect light as a mixture of these two extremes.

Few Definitions: Lambertian Reflection

• Surface reflects equally in all directions.– Examples: chalk, clay,

cloth, matte paint

• Brightness doesn’t depend on viewpoint.

• Amount of light striking surface proportional to cos θ.

LN DD KI LN DD KI

intensity

albedo surface normal

(light intensity)* (light direction)

L,0 Nmax DD KI L,0 Nmax DD KI

Few Definitions: Specular Reflection

• Specular surfaces reflect light more strongly in some directions than in others.

• Appearance of a surface depends on the direction L of the light source, direction of the surface normal N, and direction V of viewing.

The vectors L, N and R all lie in one plane

Few Definitions: Specular Reflection

• Perfect mirror: The angle of incidence equals the angle of reflection.

rough specular

RN

L

mirror

RN

L θθ

• Rough specular : Most specular surfaces reflect energy in a tight distribution (or lobe) centered on the optical reflection direction– Examples: metals,glass

NNLL

ll

RR

VVrr

nss KI cosL n

ss KI cosL

Few Definitions: Phong Model

• Determine the angle α between the direction V of viewing and the direction R of reflection by an ideal mirror.

• Assume the intensity of reflected light is proportional to cos(α)

• The exponent n (“shine”) is determined empirically.

• Large values of n make the surface behave more like an ideal mirror.

• Phong’s exponent controls how fast the highlight “falls-off”

Lambertian

Main Approaches

2D methods based on quasi-invariance to lighting

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Model- based: 3D to 2D

3D

imagerendering

Low dimensional representation of an object’s image set under different lightings

comparecompare

Main Approaches

Specular

2D Methods: will be distracted by highlights and lack of real edges.

3D Methods: Specular objects cannot be well approximated by low-dimensional linear sub-spaces.

Apply Lambertian methods and treat specularities as noise

?

Use specularities for recognition

Matching Specularities

hypothesized pose

approximate

3D model

Mapping

imageimage

Gaussian sphereGaussian sphere

Finding Specularity

query

map onto the sphere

consistent

specularity disk

map back

recovered highlights

threshold

specular candidates

Wrong Match

query

inconsistent

map onto the sphere

specularity disk

map back

recovered highlights

threshold

specular candidates

Combined Method for Recognition of General Objects

• Integrate knowledge about highlights with the Lambertian component.

• No prior knowledge of lighting.

Recover light direction from Lambertian component.

• No prior knowledge of how specular and how Lambertian the object is.

Comparison

renderrender

Lambertian Lambertian componentcomponent

highlighthighlight

Lambertian Lambertian componentcomponent

highlighthighlight

Same objectSame object

Uncontrolled Lighting

• First step: allow multiple unknown light sources.– Extend the highlight recovery to work with

known multiple light sources. – Detect multiple light source directions from the

Lambertian component.– Use both Lambertian and specular parts to

more robust detection of light sources.

PROJECT 5

Extend the specular recognition algorithm* to multiple light sources. Collect a test set of several rotationally symmetric glass objects:- Take images of these objects filled with opaque liquid for 3D model construction.- Take 3 images of each object with 2 and 3 light sources and different backgrounds.

Test the algorithm on these objects.*M. Osadchy, D.W. Jacobs and R. Ramamoorthi, Using specularities for recognition, IEEE International Conference on Computer Vision (ICCV), 2003

Multiple Light Source Detection

Given an image of known shape, recover the light sources.

)0,smax(N m

mdKI

Sphere Illumination

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0sn p

s,0nmax ppI

0sn p

0sn p

Critical Boundary

Multiple Light Sources

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3skp

Lmmpp

k

I vnsn

kL Set of lights that illuminate pixels in kR

kv Virtual light associated with region kR

kR

Finding Critical Boundaries

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wI

2

2-vN

1ww I

wf smalllarge

• Threshold f• Windows with large f

correspond to points on critical boundaries.

• Apply Hough Transform to fit points to critical boundaries.

Real light source

If two regions and are adjacent on theimage, with and the corresponding virtual lights then

1R2R

1v 2v

ms 21 vv

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PROJECTS 6

• Implement V2R algorithm* on sphere with 3 light sources (no opposite lights).

• Extend V2R algorithm to textured spherical objects.

• Large bonus: extend this algorithm to run on arbitrary convex objects.

*Christos-Savvas Bouganis, Mike Brookes. "Multiple Light Source Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26,  no. 4,  pp. 509-514,  April,  2004.