ISPRS Commission III, Vol.34, Part 3A »Photogrammetric Computer Vision“, Graz, 2002
from 3D surface points. The system consists of three cas-
caded subsystems, where the subsystems produce signal,
primitive and structural level organizations, respectively.
We attempted to fully incorporate the strategies of Object
Oriented Programming (OOP) for developing the system
software. The entire software was thus programmed with
ANSI C++ with Standard Template Library (STL). The
software involves many newly defined classes correspond-
ing to main objects and algorithms of the system, such as
points, edges, patches, surfaces, surface clusters, graphs,
LMS estimation, LMeDS estimation and others. The soft-
ware has been compiled and tested mainly under Microsoft
Windows Operating Systems (OS), but it is platform inde-
pendent.
The system is then theoretically analyzed in terms of pa-
rameter selection and computation complexity. The analy-
sis indicates that the system is robust to parameter selection
and maintains moderate computational complexity. Lee
(2002) provides further details.
3.2 Experiments
The main test areas are the sub-sites of the Ocean City test
site. A more detailed description of this test site is pre-
sented by Csatho et al. (1998). The data sets, acquired by a
LIDAR system, cover many urban areas in Ocean City. We
tested the proposed system with many sets and presented
the results of two sets among them. The main properties
of these sets are summarized in Table 4. Set A is selected
to illustrate the full system processes; and set B is used to
demonstrate the overall quality of the output over a large
area. Figure 1 and 2 show the perceptual organization gen-
erated from set A and B, respectively.
Set | No. Area Density
points | [m?] | [points/m?]
A | 4633 | 5564 0.83
B | 32493 | 32926 0.99
Table 4: Properties of the test data.
4. CONCLUSIONS
We constructed a novel approach that computes percep-
tual organization at three levels from 3D surface points
and implemented this approach as an autonomous system.
This system was tested with real LIDAR data sets of vari-
ous characteristics. The system performance was evaluated
by inspecting visually the quality of the organized output.
This evaluation strongly demonstrates the usefulness of the
proposed approach.
The proposed approach produces autonomously with mod-
erate computation loads, robust, explicit, complete, com-
putationally efficient and hierarchical descriptions from raw
surface points. The organized output thus serves as a valu-
able input to higher order perceptual processes, includ-
ing the generation and validation of hypotheses in object
recognition tasks.
Future research will concentrate on the following topics:
e To evaluate rigorously the system performance through
quantitative analysis as well as qualitative inspections,
with the input data of various ranges and characteris-
tics.
e To develop a mechanism that adjusts the system based
on domain knowledge specific to a given input and a
pursuing application.
e To apply the output to higher level processing such as
DEM generation, building reconstruction, change de-
tection, urban modelling and other object recognition
and reconstruction tasks.
References
Boyer, K. L. and Sarkar, S., 1999. Perceptual organiza-
tion in computer vision: status, challenges, and poten-
tial. Computer Vision and Image Understanding, 76(1),
pp. 1-5.
Csatho, B., Krabill, W., Lucas, J. and Schenk, T., 1998. A
multisensor data set of an urban and coastal scene. In:
International Archives of Photogrammetry and Remote
Sensing, Object Recognition Scene Classification From
Multispectral and Multisensor Pixels, Columbus, OH,
USA, Vol. 32, Part 3/2, pp. 26-31.
Edelsbrunner, H., Kirkpatrick, D. G. and Seidel, R., 1983.
On the shape of a set of points in the plane. IEEE Trans-
actions on Information Theory, 29(4), pp. 551—559.
Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P. J.,
Bunke, H., Goldgof, D. B., Bowyer, K., Eggert, D. W.,
Fitzgibbon, A. and Fisher, R. B., 1996. An experimen-
tal comparison of range image segmentation algorithms.
IEEE Transactions on Pattern Analysis and Machine In-
telligence, 18(7), pp. 673—689.
Jiang, X., Bunke, H. and Meier, U., 2000. High-level fea-
ture based range image segmentation. Image and Vision
Computing, 18(10), pp. 817—822.
Koster, K. and Spann, M., 2000. MIR: an approach to ro-
bust clustering-application to range image segmentation.
IEEE Transactions on Pattern Analysis and Machine In-
telligence, 22(5), pp. 430-444.
Lee, L, 2002. Perceptual organization of surfaces. Ph.D.
dissertation, The Ohio State University, Columbus, OH,
USA.
Lee, I. and Schenk, T., 2001. 3D perceptual organiza-
tion of laser altimetry data. In: International Archives
of Photogrammetry and Remote Sensing, Land Surface
Mapping and Characterization Using Laser Altimetry,
Annapolis, MD, USA, Vol. 34, Part 3/W4, pp. 57-65.
Liu, X. and Wang, D. L., 1999. Range image segmenta-
tion using a relaxation oscillator network. IEEE Trans-
actions on Neural Networks, 10(3), pp. 564-573.
Sarkar, S. and Boyer, K. L., 1993. Perceptual organization
in computer vision: a review and a proposal for a classi-
ficatory structure. IEEE Transactions on Systems, Man
and Cybernetics 23(2), pp. 382-399.
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