0.15 um (better than
riate accuracy for an
ody was imaged in a
siduals reached only
nera did not have a
juently the achieved
le value under the
elds residuals at the
ro-reflective targets).
surements was upon
ugh it is noteworthy
r of XYZ object point
of 1:100,000 of the
vorks showed that all
'ectly in each project,
lated. Analysis of the
wed that most targets
xcclusions or highly
ets prevented this in
>d process were very
iat the data cleansing
riangulation process
'ement, especially for
juin, Project 2.
e computed both on a
ith 128 MB RAM and
th 512MB RAM. The
s, respectively, for the
jects 1 and 2. The
he AMD system were
conds. Details can be
| times are certainly
-line VM, and it is
1 PC technology will
dle Overall
ent Process
7.3
14.3
12.1
29.9
7. CONCLUDING REMARKS
The automated image measurement and multi-stage
photogrammetric triangulation process developed for Australis
has proved both robust and computationally efficient. Not
surprisingly, optimal reliability, speed and accuracy are
typically attained with ‘high-quality’ images of retro-reflective
targets. The results obtained with ‘low-quality’ images are,
nevertheless, also reasonably robust, showing that automated
processes have potential in situations where lower contrast, non
retro-reflective targets are employed, and where the image
scanning process identifies a higher than desirable number of
candidate targets which turn out to be invalid data.
8. REFERENCES
Atkinson, K.B. (Ed.), 1996. Close Range Photogrammetry and
Machine Vision, Whittles, Scotland, 371 pages.
Chen, J., Clarke, T.A. & Robson, S., 1993. An alternative to the
epipolar line method for automatic target matching in multiple
image 3-D measurement, In: Optical 3-D Measurement
Techniques II, (Gruen, A., Kahmen, H., eds.), Wichmann,
Karlsruhe, pp. 197-204
Furnee, E.H., Jobbagy, A., Sabel, J.C., van Veenendaal, H.L.J,
Martin, F. & Andriessen, D.C.W.G., 1997. Marker-referred
movement measurement with grey-scale coordinate extraction
for high-resolution real-time 3D at 100 Hz, SPIE Proceedings,
Vol. 3173, pp 357-369.
-67—
Fraser, C.S. & Shao, J., 1997. An Image Mensuration Strategy
for Automated Vision Metrology. In: Optical 3-D Measurement
Techniques IV, (Gruen, A., Kahmen, H., eds.), Wichmann,
Heidelberg, pp. 187-197.
Fraser, C.S. & Edmundson, K.L., 2000. Design and
Implementation of a Computational Processing System for Off-
Line Digital Close-Range Photogrammetry. ISPRS Journal of
Photogrammetry & Remote Sensing, 55(2): 94-104.
Geodetic Services, Inc., 2002. Company website, accessed May:
www.geodetic.com. :
Gonzalez, R.C. & Woods, RE., 1992. Digital Image
Processing, Addison-Wesley, pp 418-423.
Luhmann, T., 2000. Nahbereichsphotogrammetrie, Grundlagen,
Methoden und Anwendungen. Wichmann, Heidelberg, 571
pages.
Otepka, J., 2001. Algorithms and their Implementation in an
Automated Close-Range Photogrammetric System, Diploma
thesis, Vienna University of Technology.
Sabel, fe 1999. Calibration and 3D Reconstruction for Multi
Camera Marker Based Motion Measurement, PhD thesis,
Faculty of Applied Physics, Technical University of Delft,
Netherlands.
Shortis, M.R., Clarke, T.A. & Short, T., 1994, Comparison of
some techniques for the subpixel location of discrete target
images, SPIE Proceedings, Vol. 2350, Paper 425.