It is shown that an efficient practical registration algorithm previously described by H.G. Barrow et al. (1977) can provide high-accuracy registration. Experiments with a quantized video image of a solid triangle yielded registration that was accurate to 3% of the interpixel spacing (i.e. accurate to 0.5 mils) in the x and y directions and 0.015 degrees in rotation. It may also be important to predict the accuracy in advance, to see whether specifications can be met and to estimate accuracy during registration, in order to control quality. The authors provide practical formulas for both purposes for two kinds of image point data: edge detection (ED) data and direct measurement (DM) data. In two experiments using ED data, the predicted, estimated, and observed accuracies are all in agreement. The prediction theory developed suggests five precautions to avoid loss of registration accuracy. Perhaps most important is Precaution c, the necessity of the ED case not to have a large fraction of the total segment length of the model aligned with the horizontal or vertical directions of the pixel grid. When the model consists largely of horizontal and vertical segments, a good way to observe this precaution is to tilt the pixel grid a few degrees away from perfect alignment, e.g. by tilting the video camera. A third experiment verifies that violating Precaution c can seriously degrade accuracy.>

An algorithm is described for matching two-dimensional images that does not depend on the extraction of features, a step which is often difficult or impractical. It combines computational efficiency with robustness against noisy data. It has been tested with two kinds of data. The algorithm matches a two-dimensional image, in the form of a set of points, to a line segment model. It estimates a positive congruence (i.e., a translation plus a rotation) that moves the image onto the model. The algorithm is most appropriate for matching when the required congruence is small, and it seems very well suited to motion-tracking. Experimental results have been obtained for two kinds of images: (1) points obtained from an optical range finder, and (2) edge points extracted from an intensity image. Only the former results are presented.

A recent paper by Sammon1 describes a useful technique for analyzing data and helping find clusters. It consists of a nonlinear mapping of the original data into two dimensions (for visual inspection) which is superior for many purposes to use of the first two principal components. The present correspondence points out that essentially the same mapping can be achieved by a widely used, easily obtainable computer propram for multidimensional scaling called M-D-SCAL.