Learning OpenCV

Subpixel Corners

If you are processing images for the purpose of extracting geometric measurements, as opposed to extracting features for recognition, then you will normally need more resolution than the simple pixel values supplied by cvGoodFeaturesToTrack(). Another way of saying this is that such pixels come with integer coordinates whereas we sometimes require real-valued coordinates—for example, pixel (8.25, 117.16).

One might imagine needing to look for a sharp peak in image values, only to be frustrated by the fact that the peak's location will almost never be in the exact center of a camera pixel element. To overcome this, you might fit a curve (say, a parabola) to the image values and then use a little math to find where the peak occurred between the pixels. Subpixel detection techniques are all about tricks like this (for a review and newer techniques, see Lucchese [Lucchese02] and Chen [Chen05]). Common uses of image measurements are tracking for three-dimensional reconstruction, calibrating a camera, warping partially overlapping views of a scene to stitch them together in the most natural way, and finding an external signal such as precise location of a building in a satellite image.

Subpixel corner locations are a common measurement used in camera calibration or when tracking to reconstruct the camera's path or the three-dimensional structure of a tracked object. Now that we know how to find corner locations on the integer grid of pixels, here's the trick for refining ...