The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.08 - August (1990 vol.12)
pp: 721-734
ABSTRACT
<p>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.</p>
INDEX TERMS
computer vision; accuracy; subpixel registration algorithm; quantized video image; solid triangle; image point data; edge detection; direct measurement; prediction theory; computer vision; estimation theory; iterative methods
CITATION
I.J. Cox, J.B. Kruskal, D.A. Wallach, "Predicting and Estimating the Accuracy of a Subpixel Registration Algorithm", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.12, no. 8, pp. 721-734, August 1990, doi:10.1109/34.57665
25 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool