EFFICIENT METHOD FOR FINDING THE POSITION OF OBJECT BOUNDARIES TO SUB-PIXEL PRECISION

A new class of algorithms is described for the analysis boundaries in a discrete image. The simplest algorithm of this class accepts as input an initial estimate for the boundary position and, after a process of iterative refinement, outputs a more accurate estimate. The method is an extension of known methods for edge detection which are based on Gaussian filtering. Instead of using a discrete filter and exhaustive evaluation, the filter output is computed only at isolated points. These points are selected by a numerical optimization routine to converge on the feature of interest. Because in general the points do not coincide with pixels the filter must be reconstructed between pixels from the discrete image data. This method permits the measurements to be made to sub-pixel accuracy without the need for a mathematical model of the boundary. The reliability of the method is discussed in terms of the pixel size (sampling error) and the size and proximity of clutter relative to the size of the filter. The accuracy is related to the filter size. Possible applications include non-contact measurement, and an example is given.