displacements, that digitizing would include pointing error,
and the displacement computed from the digitized coordinates
would include the effects of pointing error. For purposes
of testing the matching algorithms, features were chosen
that contained discrete points and edges to reduce this
source of error as much as possible.
6. CONCLUSIONS
As indicated by the feature matching results, the boundary
detection and matching algorithm developed here can compute
anatomic feature displacement to sub-pixel accuracy on
Sequential cranial x-ray films. The use of a feature
extraction algorithm serves to reduce the background X-ray
noise, and produces a discrete data set for feature
matching. The use of a six parameter boundary transformation
successfully compensates for the non-orthogonality of
feature growth over the two year period.
Although the accuracies are not high by photogrammetric
standards, they are satisfactory for biostereometric
Purposes, and are partly a function of the low resolution of
the array camera used. The techniques described in this
report would find application in feature matching on aerial
photographs where features have undergone distortion due to
morphological change. However, it is limited to those cases
where discrete feature boundaries can be extracted.
7. REFERENCES
/1/ Ackermann, F.: Digital Image Correlation :
Performance and Potential
Applications.
The Photogrammetric Record, Vol
1, No. 64, (1984), pp. 429-440.
/2/- Curry, S.., Baumrind, Analysis of Stereo Cranial X-Rays
Using Digital Images.
Close Range Photogrammetry and
Surveying Symposium, ASP, pp. 35-
46.
/3/ Curry, 8., Anderson, Calibration of An Array Camera.
J.M., Baumrind, S.: Photogrammetric Engineering and
Remote Sensing, in press.
/A/ Pavlidis, T. : Algorithms for Graphics and Image
Processing.
Computer Science Press, 1982.
/5/ Rosenfeld, A, : Image Analysis : Problems,
Progress, and Prospects.
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