Features-based Object Recognition

Pierre MoreelsCalifornia Institute of Technology

Thesis defense, Sept. 24, 2007

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The recognition continuumvariab

ility

Individual objects

means of transportation

BMW logo

Categories

cars

Applications

Autonomousnavigation

Identification, Security.

Help Daiki find his toys !

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• Problem setup

• Features

• Coarse-to-fine algorithm

• Probabilistic model

• Experiments

• Conclusion

Outline

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…

The detection problem

New scene (test image)

Models fromdatabase

Find models and their pose (location, orientation…)

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…

Hypotheses – models + positions

New scene (test image)

Models fromdatabase

1

2

Θ = affine transformation

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…

Matching features

Models fromdatabase

New scene (test image)

Set of correspondences = assignment vector

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Features detection

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Image characterization by features

• Features = high information content

‘locations in the image where the signal changes two-dimensionally’ C.Schmid

• Reduce the volume of information

edge strength map

features

– [Sobel 68]– Diff of Gaussians [Crowley84]– [Harris 88]– [Foerstner94]– Entropy [Kadir&Brady01]

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Correct vs incorrect descriptors matches

Mutual Euclidean distances in appearance space of descriptors

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34

5

6

7

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- Pixels intensity within a patch- Steerable filters [Freeman1991]- SIFT [Lowe1999,2004]- Shape context [Belongie2002]- Spin [Johnson1999]- HOG [Dalal2005]

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Stability with respect to nuisances

Which detector / descriptor

combination is best for recognition ?

Past work on evaluation of features• Use of flat surfaces, ground truth easily established• In 3D images appearance changes more !

[Schmid&Mohr00] [Mikolajczyk&Schmid 03,05,05]

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Database : 100 3D objects

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Testing setup

[Moreels&Perona ICCV05, IJCV07]

Used by [Winder, CVPR07]

Results – viewpoint change M

ahal

anob

is d

ista

nce

No

‘bac

kgro

und’

imag

es

2D vs. 3D

Ranking of detectors/descriptorscombinations are modified whenswitching from 2D to 3D objects

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Features matching algorithm

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Features assignments

models from database

New scene (test image)

. . .

Interpretation

. . .

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Coarse-to-fine strategy• We do it every day !

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my car

Coarse-to-fine example

[Fleuret & Geman 2001,2002]

Face identification in complex scenes

Coarse resolution

Intermediate resolution

Fine resolution

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• Progressively narrow down focus on correct region of hypothesis space

• Reject with little computation cost irrelevant regions of search space

• Use first information that is easy to obtain

• Simple building blocks organized in a cascade

• Probabilistic interpretation of each step

Coarse-to-Fine detection

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Coarse data : prior knowledge

• Which objects are likely to be there, which pose are they likely to have ?

unlikelysituations

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New scene (test image)…

Models fromdatabase

4 votes

2 votes

0 vote

Model voting

Search tree (appearance space – leaves = database features)

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(x1,y1,s1,1)

(x2,y2,s2,2)

Transform predicted by this match: x = x2-x1

y = y2-y1

s = s2 / s1

= 2 - 1

Each match is represented by a dot in

the space of 2D similarities (Hough space)

x

y

s

Use of rich geometric information

[Lowe1999,2004]

• Prediction of position of model center after transform

• The space of transform parameters is discretized into ‘bins’

• Coarse bins to limit boundary issues and have a low false-alarm rate for this stage

• We count the number of votes collected by each bin.

Coarse Hough transform

N~

Model

Test scene

correct transformation

26Output of PROSAC : pose transformation

+ set of features correspondences

Correspondence or clutter ? PROSAC

• Similar to RANSAC – robust statistic for parameter estimation

• Priority to candidates with good quality of appearance match

• 2D affine transform : 6 parameters

each sample contains 3 candidate correspondences.

d

d

d

[Fischler 1973] [Chum&Matas 2005]

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Probabilistic model

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Generative model

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Recognition steps

Score of an extended hypothesis

Hypothesis:model + position

observed featuresgeometry + appearance

database of models

constant

Consistency(after PROSAC)Prior on model

and poses

Featuresassignments

Votes per model Votes per model pose bin(Hough transform)

Prior on assignments(before actual observations)

ConsistencyConsistency between observations and predictions from hypothesis

model m

position of model m

Common-frame approximation : parts are conditionally independent once reference position of the object is fixed. [Lowe1999,Huttenlocher90,Moreels04]

Con

stel

latio

n m

odel

Com

mon

-fra

me

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foreground features ‘null’ assignments

geometry geometryappearance appearance

Consistency - appearance Consistency - geometry

ConsistencyConsistency between observations and predictions from hypothesis

Learning foreground & background densities

• Ground truth pairs of matches are collected

• Gaussian densities, centered on the nomimal value that appearance / pose should have according to H

• Learning background densities is easy: match to random images.

[Moreels&Perona, IJCV, 2007]

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Experiments

An example

Model votin

g

Hough

bins

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An example

After

PROSAC

Probabilistic

scores

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Efficiency of coarse-to-fine processing

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Giuseppe Toys database – Models

61 objects, 1-2 views/object

Giuseppe Toys database – Test scenes

141 test scenes

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Home objects database – Models

49 objects, 1-2 views/object

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Home objects database – Test scenes

141 test scenes

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Results – Giuseppe Toys database

Lowe’99,’04

Lower false alarmrate- more systematic verification of geometry consistency- more consistent verification of geometric consistency

undetected objects: features with poor appearance distinctivenessindex to incorrect models

-

+

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Results – Home objects database

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Failure modeTest image hand-labeledbefore the experiments

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Test – Text and graphics

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Test – no texture

Test – Clutter

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• Coarse-to-fine strategy prunes irrelevant search branches at early stages.

• Probabilistic interpretation of each step.

• Higher performance than Lowe, especially in cluttered environment.

• Front end (features) needs more work for smooth or shiny surfaces.

Conclusions