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FIGURE 8.8
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Optical character recognition.
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characters are normally presented to the recognition system in a relatively constrained orientation, a measurement of which is not required.
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It is a natural requirement that the visual system for an assembly machine should be able to accommodate a number of components randomly positioned in the field of view. The corresponding problem of segmentation in character recognition is eased (for printed characters) by a priori knowledge of character size and pitch. Such information has fostered techniques for the segmentation of touching characters. No attempt is made to distinguish between touching objects. Their combined image will be treated by the identification procedures as that of a single, supposedly unknown object. The essentially unlimited size of the set of objects that must be accommodated by the recognition system demands that a detailed description of shapes be extracted for each image. There are, however, a number of basic parameters which may be derived from an arbitrary shape to provide valuable classification and position information. These include: Area Perimeter Minimum enclosing rectangle Center of the area Minimum radius vector (length and direction) Maximum radius vector (length and direction) Holes (number, size, position) Measurements of area and perimeter provide simple classification criteria that are both position- and orientation-invariant. The dimensionless shape factor area/perimeter has been used as a parameter in object recognition. The coordinates of the minimum enclosing rectangle provide some information about the size and shape of the object, but this information is orientation-dependent. The center of area is a point that may be readily determined for any object, independent of orientation, and is thus of considerable importance for recognition and location purposes. It provides the origin for the radius vector, defined as a line in the center of the area to a point on the edge of an object. The radius vectors of maximum and minimum length are potentially useful parameters for determining both identification and orientation. Holes are common features of engineering components, and the number present in a part is a further suitable parameter. The holes themselves may also be treated as objects, having shape, size, and position relative to the object in which they are found.
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The requirements for the establishment of connectivity in the image and the derivation of detailed descriptions of arbitrary geometric shapes are most appropriately met by an edge-following technique. The technique starts with the location of an arbitrary point on the black/white edge of an object in the image (usually by a raster scan). An algorithm is then applied that locates successive connected points on the edge until the complete circumference has been traced and the starting point is reached. If the direction of each edge point relative to the previous point is recorded, a one-dimensional description of the object is built up, which contains all the information present in the original shape. Such chains of directions have been extensively studied by Freeman. Measurements of area, perimeter, center of area, and enclosing rectangle may be produced while the edge is being traced, and the resulting edge description is in a form convenient for the calculation of radius vectors. Edge following establishes connectivity for the object being traced. Continuing the raster scan in search of further objects in the stored image then presents the problem of the rediscovery of an already traced edge. A computer plot of the contents of the frame with the camera viewing a square and a disk is illustrated in Fig. 8.9, and the result of applying the edge-extracting operation is illustrated in Fig. 8.10. The edge-following procedure may now be applied to the image in the same way it was to the solid object. The procedure is arranged,
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