The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008
While the overall segmentation features improvement to the
basic ones, both features suggest that the segmentation along the
proposed scheme can be further pursued in several directions.
These include inclusion of additional cues that may feature
other elements characterizing natural scenes, introducing
merging schemes for segments based on connectivity and forms
of similarity, and the analysis of occlusions as a means merge
disconnected segments that belong in fact to the same object.
4. CONCLUDING REMARKS
The paper proposed an approach for the segmentation of
terrestrial laser point clouds while assembling and integrating
different data sources. The proposed model offers a general
framework in the sense that it can utilize different features and
can be customized according to application requirements.
Overall, the results show that integration of different cues and
information sources into a laser scanning segmentation has
managed providing improved results in relation to each of the
individual channels.
The model demonstrated that using an intuitive scheme for
selecting the best segments from different segmentation maps
provides satisfactory results. The solution for weighting the
importance of different cues to the overall segmentation is
modeled as a crisp decision favoring dominant segments in
object space as long as they do not violate preset rules. Future
work in this regards will pursue alternative weighting schemes
for the data arriving from the individual channels.
ACKNWOLEDGEMENT
The authors would like to thank Dr. Claus Brenner for making
the data used for our tests available.
REFERENCES
Alpert S., Galun M., Basri R., Brandt A., 2007. Image
segmentation by probabilistic bottom-up aggregation and cue
integration. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). Minneapolis, June 2007.
Bleyer, M., and Gelautz, M., 2004. A layered stereo algorithm
using image segmentation and global visibility constraints, in
Proc. of ICIP 2004 5: 2997-3000.
Brandt A., 1986. Algebraic multigrid theory: the symmetric
case. Appl. Math. Comput, 19 pp. 23-56.
Carson, C. Belongie, S. Greenspan, H. Malik, J. 2002.
Blobworld: image segmentation using expectation-
maximization and its application to image querying. IEEE
Transactions on Pattern Analysis and Machine Intelligence
24(8): 1026- 1038.
Coiras, E., Santamaria, J., Miravet, C., 2000. Segment-based
registration technique for visual-infrared images. Optical
Engineering 39(1) pp. 282-289.
Comaniciu D., Meer. P., 2002. Mean shift: A robust approach
toward feature space analysis. IEEE transactions on PAMI,
24:603-19.
Dold, C., Brenner, C., 2006. Registration of Terrestrial Laser
Scanning Data using Planar Patches and Image Data, in: H.-G.
Maas, D. Schneider (Eds.), ISPRS Comm. V, IAPRS Vol.
XXXVI Part. 5 pp. 78-83.
Estrada F. J., Jepson A. D., 2005. Quantitative Evaluation of a
Novel Image Segmentation Algorithm. CVPR (2) 2005: 1132-
1139.
Felzenszwalb, P.F., Huttenlocher, D.P., 2004. Efficient Graph-
Based Image Segmentation, International journal of computer
vision 59(2) pp. 167-181.
Gorte B. 2007. Planar feature extraction in terrestrial laser scans
using gradient based range image segmentation, ISPRS
Workshop on Laser Scanning, pp. 173-177.
Hartley R., Zisserman A., 2003. Multiple View Geometry in
Computer Vision. Cambridge University Press, Second Edition.
Huang, Q., Wen G., Wenjian C. 2005 Thresholding technique
with adaptive window selection for uneven lighting image.
Pattern Recognition Letters 26 pp. 801-808.
Klaus A., Sormann M., and Kamer K., 2006 Segment-based
stereo matching using belief propagation and a self-adapting
dissimilarity measure, in Proc. of ICPR 2006, pp. 15-18.
Mian, A., Bennamoun, M., Owens, R., 2006. Three-
Dimensional Model-Based Object Recognition and
Segmentation in Cluttered Scenes. IEEE transactions on PAMI.
28(10), 1584-1601.
Otsu N. 1979. A threshold selection method from gray-level
histograms. IEEE Trans. Sys., Man., Cyber. 9: 62-66.
Pal N. R. and Pal S. K., 1993. "A review on image segmentation
techniques," Pattern Recognition, 26(9): 1277-1294, 1993.
Rabanni T., 2006. Automatic reconstruction of Industrial
Installations using point clouds and images. PhD thesis. NCG,
publication on Geodesy 62.
Roth, V., Ommer, B., 2006. Exploiting Low-Level Image
Segmentation for Object Recognition, DAGM06 pp. 11-20.
Russell, C. B., Efros A., Sivic J., Freeman T. W., Zisserman
A., 2006. Using Multiple Segmentations to Discover Objects
and their Extent in Image Collections, in Proc. of CVPR 2006 2,
1605-1614.
Sharon E., Brandt A., Basri R., 2000. Fast Multiscale Image
Segmentation. CVPR 2000: 1070-1077.
Shi J., Malik J., 2000. Normalized Cuts and Image
Segmentation. IEEE transactions on PAMI. 22(8): 888-905.
Vosselman G., S. Dijkman, 2001. 3D Building model
reconstruction from point clouds and ground plans. IAPRS
34(3/W4).