Speculating, not speaking from direct experience, I'd expect that a distinctively colored circle would work best, especially if low resolution and processing power are important, the visual target is reasonably large (at least 5 or 10 pixels high or wide), and high precision is not required.
Speaking from experience, use HSV (*1) or a similar color scheme to detect the circle. Threshold (separate object pixels from background pixels) based on a formula like object=[abs(hue-orange)0.7 AND value>0.4]. Hunter's orange is easily identified in the average indoor setting. For microprocessor efficiency, rescale the HSV equations to values of 0-255.
If such a circle is viewed "full frontal", then you will see a circle; if viewed at an angle, you will see an ellipse. You can easily calculate this ellipse using the mean and moments of the pixel coordinates (*2). Based on these axes, you can determine the axis of tilt (major axis of the ellipse).
If you also need orientation, then a second or third uniquely colored circle can be used. Looking at the vectors between the centers of these circles will yield orientation information, in addition to the previously obtained tilt.
I've seen other algorithms which rely on a checker-board pattern. They use edge detection to fit lines across the board. These lines are then used to calculate the board's position and orientation. These also seemed more processor intensive.
Have fun, Daniel
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