Black Spot Firmware
Using Figure 4.8 and Equation 4.6, preal
= an + vn + pn.
In this case
Substituting Equation 4.9 and Equation 4.7 into Equation 4.10 gives
= |an| .
In Figure 4.8, it is the maximum marker acceleration amax
that defines smax
. Equating the
magnitude of this acceleration, a
, to that observed in Chapter3 gives
= |amax| = a
in Equation 4.8 gives
which states that, using this prediction scheme, the minimum ROI size required to track the typical scanner motion found in Chapter 3 is proportional to the magnitude of the max- imum acceleration. This was determined to be approximately 8 pixels / frame2 meaning that the maximum ROI size required for a diameter of 16 pixels (Equation 3.24) is 32 × 32.
This prediction algorithm is a limiting case of a discrete Kalman Filter [19, 66]. The system state is the position pn measured by the centroid calculation. The state prediction step is Equation 4.7. In this case, the centroid calculation is used directly to give the state estimate.
There are other algorithms that could improve the performance of the Black Spot module. They are proposed for future implementation.
The first algorithm is a marker discriminator algorithm. Ideally the module should be able to distinguish between a real marker and noise sources such as bright lights and reflections. lso, markers should be able to be ignored if they are too close or too far away to be useful. The design of this algorithm has not been investigated but the characteristics of marker images may be able to be used to determine whether a region of bright pixels is a marker
or an artefact.
possible, although computationally intensive, approach would be to use