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By John G. Webster (Editor)

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Regions that have been separated generally require postprocessing before they are supplied to the labeling system’s input. Thresholding and texture segmentation algorithms may be corrupted by noise, which must be eliminated with a filter. Edge detectors leave regions that have boundaries greater than a single pixel thick and often have discontinuities, which must be eliminated. There are two general approaches for postprocessing edge detection boundaries: first, to interpret all the edges as geometric boundaries and, secondly, to create a digital boundary.

Although traditional best-fit algorithms usually deal with a functional approximation for a given set of data, they can be extended to computer vision. If the pixels along an edge are considered data points, it is possible to find a curve that approximates them. Examples of traditional curve-fitting algorithms are least mean squares, linear interpolation, and splining. For brevity, we will deal only with fitting lines to the data. Depending on the required accuracy of the representation, all the points generated by the search algorithm may not be required to obtain an accurate geometric description of the contour.

The feature extraction stage may also perform some preliminary object recognition by filtering out objects that do not satisfy broad criteria for the object of interest. For example, the feature extractor might return only square shapes if the recognition system were looking for a specific type of building in a satellite image, or the extraction step may return only large objects if the image is likely to be marred by small speckles of noise. Object recognition may also proceed in several steps, where first geometric primitives are identified and then the primitives are fed through another classifier that determines what object the primitives represent.

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16.Control Systems Technology by John G. Webster (Editor)


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