auditory and visual spatial sensing

Auditory and Visual Spatial Sensing

Stan BirchfieldDepartment of Electrical and

Computer EngineeringClemson University

Human Spatial Sensing

The five senses:

Hearing

Taste

Touch

Smell

Seeing

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

Visual and Auditory Pathways

Two Problems inSpatial Sensing

Stereo Vision Acoustic Localization

Clemson Vision Laboratory

head tracking

root detection reconstruction

highway monitoring

motion segmentation

Clemson Vision Lab (cont.)

microphone position calibration

speakerlocalization

Stereo Vision

INPUT

OUTPUT

Left Right

Disparity map Depth discontinuities

epipolarconstraint

Epipolar Constraint

Left camera Right camera

world point

center ofprojection

epipolarplane

epipolarline

Energy Minimization

Left

Right

inte

nsi

ty occluded pixels

E E d(x ,x - ) u(l )data smoothness L Lx iiL

minimize:

dissimilarity discontinuitypenalty

(underconstrained)constraint

History of Stereo Correspondence

Birchfield & Tomasi 1998

Geiger et al. 1995

Intille &Bobick 1994

Belhumeur & Mumford 1992

Ohta & Kanade 1985

Baker & Binford 1981

MULTIWAY-CUT(2D)

DYNAMICPROGRAMMING

(1D)

Kolmogorov & Zabih 2001, 2002

Lin & Tomasi 2002

Birchfield & Tomasi 1999

Boykov, Veksler, and Zabih 1998

Roy & Cox 1998

Dynamic Programming: 1D Search

Dis

par

ity

map

occlusion

depthdiscontinuity

RIGHTL

EF

T

c a r t

ca

t 3 2 1 1 12 1 0 1 21 0 1 2 30 1 2 3 4

string editing:

stereo matching:

penalties: mismatch = 1 insertion = 1 deletion = 1

c a t

c a r t

Multiway-Cut:2D Search

pixels

labels

pixels

labels

[Boykov, Veksler, Zabih 1998]

Multiway-Cut Algorithm

),( x'x ))(, x(x fg

minimum cut

),(

)]()()[,())(,x'xx

x'xx'xx(x fffg Minimizes

source label

sink label

pixels

(cost of label discontinuity)

(cost of assigninglabel to pixel)

pixels

labels

Sampling-InsensitivePixel Dissimilarity

d(xL,xR)

xL xR

d(xL,xR) = min{d(xL,xR) ,d(xR,xL)}Our dissimilarity measure:

[Birchfield & Tomasi 1998]

IL IR

Given: An interval A such that [xL – ½ , xL + ½] _ A, and

[xR – ½ , xR + ½] _ A

Dissimilarity Measure Theorems

If | xL – xR | ≤ ½, then d(xL,xR) = 0

| xL – xR | ≤ ½ iff d(xL,xR) = 0

∩∩

Theorem 1:

Theorem 2:

(when A is convex or concave)

(when A is linear)

Correspondence as Segmentation

• Problem: disparities (fronto-parallel) O()surfaces (slanted) O( 2 n)=> computationally intractable!

• Solution: iteratively determine which labels to use

labelpixels

find affineparametersof regions

multiway-cut(Expectation)

Newton-Raphson(Maximization)

Stereo Results (Dynamic Programming)

Stereo Results (Multiway-Cut)

Stereo Results on Middlebury Database

imag

eB

irch

fiel

dT

om

asi 1

999

Ho

ng

-C

hen

200

4

Multiway-Cut Challenges

Multiway-cutDynamic programming

Acoustic Localization

Problem: Use microphone signals to determine sound source location

Traditional solutions:1. Delay-and-sum beamforming !2. Time-delay estimation (TDE) !

compact

distributed

Recent solutions:3. Hemisphere sampling !!4. Accumulated correlation !!5. Bayesian !6. Zero-energy !

! efficient ! accurate

Localization Geometry

t2

t1

t -2 t = 1

(one-half hyperboloid)

microphones

sound source

time

Principle of Least Commitment

“Delay decisions as long as possible”

Example:

[Marr 1982 Russell & Norvig 1995]

Localization by Beamforming

mic 1 signaldelay

mic 2 signal

prefilter

prefilter

mic 3 signal

find peak

mic 4 signal

prefilter

prefilter

sum

delay

delay

delay

[Silverman &Kirtman 1992; Duraiswami et al. 2001; Ward & Williamson, 2002]

energy

! accurate NOT efficient

makes decision late in pipeline(“principle of least commitment”)

delays (shifts) each signalfor each candidate location

Localization by Time-Delay Estimation (TDE)

mic 1 signal

correlatefind peakmic 2 signal

prefilter

prefilter

mic 3 signal

correlatefind peakmic 4 signal

prefilter

prefilter

intersect

(may be no intersection)

[Brandstein et al. 1995;

Brandstein & Silverman 1997;

Wang & Chu 1997]

! efficient NOT accurate

decision is made early

cross-correlation computed once for each microphone pair

Localization by Hemisphere Sampling

mic 1 signalcorrelate

map to common

coordinate system

sampled locus

sum

temporalsmoothing

mic 2 signal

prefilter

prefilter

mic 3 signalcorrelate

map to common

coordinate system

mic 4 signal

prefilter

prefilter

finalsampled

locus

correlate

correlate

correlate

correlate

… find peak

[Birchfield & Gillmor 2001]! efficient! accurate

(but restricted to compact arrays)

Localization by Accumulated Correlation

mic 1 signalcorrelate

map to common

coordinate system

sampled locus

sum

temporalsmoothing

mic 2 signal

prefilter

prefilter

mic 3 signalcorrelate

map to common

coordinate system

mic 4 signal

prefilter

prefilter

finalsampled

locus

correlate

correlate

correlate

correlate

… find peak

[Birchfield & Gillmor 2002]! efficient! accurate

Accumulated Correlation Algorithm

microphone

candidatelocation

= likelihood

+

...

pair 1:

pair 2:

+

Comparison

Bayesian:

Zero energy:

Acc corr:

Hem samp:

TDE:

similarity energy

efficient

accurate

Beamforming:

Unifying framework

efficient

accurate

Integration limits

BeamformingBayesianZero energy

Accumulated correlationHemisphere samplingTime-delay estimation

Compact Microphone Array

microphone

d=15cm

sampled hemisphere

Results on compact array

pan

tilt

without PHAT prefilter with PHAT prefilter

More Comparison

Hemisphere Sampling[Birchfield & Gillmor 2001]

BeamformingAccumulatedCorrelation

[Birchfield & Gillmor 2002]

Results on distributed array

Computational efficiency

0

1000

2000

3000

4000

5000

6000

7000

8000

Compact Distributed

Beamforming

Accumulatedcorrelation

Co

mp

uti

ng

tim

e p

er w

ind

ow

(m

s)

(600x faster) (50x faster)

Simultaneous Speakers

+ =

Detecting Noise Sourcesbackground noise source

Connection with Stereo

[Okutomi & Kanade 1993]

“Multi-baseline stereo”

Conclusion

• Spatial sensing achieved by arrays of visual and auditory sensors

• Stereo vision– match visual signals from multiple cameras– recent breakthrough: multiway-cut– limitations of multiway-cut

• Acoustic localization– match acoustic signals from multiple microphones– recent breakthrough: accumulated correlation– connection with multi-baseline stereo