09.06.2013 Views

Gesture-Based Interaction with Time-of-Flight Cameras

Gesture-Based Interaction with Time-of-Flight Cameras

Gesture-Based Interaction with Time-of-Flight Cameras

SHOW MORE
SHOW LESS

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

allow arbitrary range maps to be used, but they are all somewhat ad-hoc.<br />

4.2. METHOD<br />

Our approach to improving the accuracy <strong>of</strong> the range map using the shading con-<br />

straint is based on a probabilistic model <strong>of</strong> the image formation process. We obtain<br />

a maximum a posteriori estimate for the range map using a numerical minimiza-<br />

tion technique. The approach has a solid theoretical foundation and incorporates<br />

the sensor-based range information and the shading constraint in a single model;<br />

for details, see Section 4.2. The method delivers robust estimation results on both<br />

synthetic and natural images, as we show in Section 4.3.<br />

4.2 Method<br />

4.2.1 Probabilistic Image Formation Model<br />

We seek to find the range map R that maximizes the posterior probability<br />

p(X R , X I |R, A) p(R) p(A).<br />

✞ ☎<br />

✝4.1<br />

✆<br />

p(X R , X I |R, A) is the probability <strong>of</strong> observing a range map X R and an intensity image<br />

X I given that the true range map describing the shape <strong>of</strong> the imaged object is R and<br />

that the parameters <strong>of</strong> the reflectance model are A. Typically, A is the albedo <strong>of</strong> the<br />

object – we will discuss this in more detail below. p(R) is a prior on the range map,<br />

p(A) is a prior on the reflectance model parameters.<br />

The conditional probability p(X R , X I |R, A) is based on the following model <strong>of</strong><br />

image formation: First <strong>of</strong> all, we assume that p(X R , X I |R, A) can be written as fol-<br />

lows:<br />

p(X R , X I |R, A) = p(X R |R, A) p(X I |R, A).<br />

✞ ☎<br />

✝4.2<br />

✆<br />

In other words, the observations X R and X I are conditionally independent given R<br />

and A.<br />

We now assume that the observed range map X R is simply the true range map R<br />

<strong>with</strong> additive Gaussian noise, i.e.<br />

p(X R |R, A) = N (X R − R|µ = 0, σR(R, A)).<br />

✞ ☎<br />

✝4.3<br />

✆<br />

Note that the standard deviation σR is not constant but can vary per pixel as a func-<br />

tion <strong>of</strong> range and albedo. As we will see in Section 4.2.4, the noise in the range mea-<br />

surement <strong>of</strong> a TOF camera depends on the amount <strong>of</strong> light that returns to the camera.<br />

The shading constraint postulates that a given range map R is associated <strong>with</strong><br />

35

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!