Geometria hidráulica de seções transversais do rio dos Bugres
UNIVERSIDADE ESTADUAL DE MATO GROSSO FACULDADE DE CIÊNCIAS EXATAS CAMPUS DE BARRA DO BUGRES ROOF...
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Transcript of UNIVERSIDADE ESTADUAL DE MATO GROSSO FACULDADE DE CIÊNCIAS EXATAS CAMPUS DE BARRA DO BUGRES ROOF...
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UNIVERSIDADE ESTADUAL DE MATO GROSSOFACULDADE DE CIÊNCIAS EXATASCAMPUS DE BARRA DO BUGRES
ROOF CONTOURS RECOGNITION USING LIDAR DATA AND MARKOV RANDOM FIELD
MODEL ON GRAPH THEORY
AuthorEdinéia Aparecida dos Santos Galvanin
Aluir Porfírio Dal Poz
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LiDAR technology has become common in
recent years, allowing rapid and efficient
acquisition of the Digital Elevation Models
Motivation Methodology Results Conclusion
Object segmentation in urban areas, due to
scene complexity, requires the development of
specific methods that integrate the
neighborhood information and the domain
knowledge of characteristics of the interest
objects
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Introduction Methodology Results Conclusion
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Object extraction and generation of Digital
Terrain Model
Introduction Methodology Results Conclusion
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This paper proposes a methodology for
automatic extraction of building roof contours
using a graph-based MRF.
Introduction Methodology Results Conclusion
Main advantage is to provide a general and
natural model for the interaction among
spatially related random variables in the
image.
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The proposed methodology comprises the following preprocessing steps:
Introduction Methodology Results Conclusion
High regions
roof properties definition
Energy function
Minimization
Stability Roof contoursyes
not
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Recursive splitting region segmentation, region merging , vectorization and polygonization
Introduction Methodology Results Conclusion
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Introduction Methodology Results Conclusion
High regions
roof properties definition
Energy function
Minimization
Stability Roof contoursyes
not
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Introduction Methodology Results Conclusion
Using the available contours, a region adjacency graph (RAG) is constructed
The neighbourhood ,regions neighboring is defined as,
iR
rR,Rdist|R ijjr,Ri
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Introduction Methodology Results Conclusion
The construction of the energy function depends on a prior knowledge of the properties of the object ´roof´.
The features for the first order clique used is the area and rectangularity.
The area feature allows small object in relation to roofs, can be discarded.
senR
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Introduction Methodology Results Conclusion
The third attribute allows the verification of parallelism or perpendicularity between objects
Because if either (objects with parallel main axes) or if (objects with perpendicular main axes).
, 0i j
, 90i j
( , ) (2 )i j ijR R sen
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Introduction Methodology Results Conclusion
High regions
roof properties definition
Energy function
Minimization
Stability Roof contoursyes
not
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Introduction Methodology Results Conclusion
weights that gives relative importance to each term of energy
1 1 1 ( , )
1
(1 )(1 ) (2 )
ln 1 ln 1
n n ni
i i j ijii i i j G
n
i i i ii
pU r p p sen
A
p p p p
rectangularity parameter of the object .
the area of object
angle between the main axes of objects
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Introduction Methodology Results Conclusion
High regions
roof properties definition
Energy function
Minimization
Stability Roof contoursyes
not
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Introduction Methodology Results Conclusion
three-dimensional visualization of the DEM used in the test
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Introduction Methodology Results Conclusion
The extracted polygons are overlaid in red on the intensity image. This figure also shows the reference polygons (in blue) and a false negative (in green).
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Introduction Methodology Results Conclusion
The choice of test area took into account the complexity of the configurations of objects in the scene
In general, a good indication of robustness of the proposed methodology was the lack of false positives and the verification of few false negatives.The completeness parameters showed that the extracted polygons generally have high superposition with their reference polygons.
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REFERENCES
Dubes, R. C., Jain, A. K., 1989. Random Field Models in Image Analysis. Journal of applied Statistics, v. 16, n. 2, pp. 131–164. Haala, N., Brenner, C., 1999. Extraction of buildings and trees in urban environments. ISPRS Journal of Photogrammetry e Remote Sensing, v.54, pp.130-137. Jain, R. Kasturi, R & Schunck, B. G., 1995. Machine vision. MIT Press and McGraw-Hill, Inc New York. Kinderman, R., Snell, J. L. 1980. Markov Random Fields and their applications. Providence, R.I: American Mathematical Society. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P. 1983. Optimization by Simulated Annealing, Science, pp. 671–680. Kopparapu, S. K., Desai, U. B. 2001. Bayesian approach to image interpretation. 127p.
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THANKS FOR ATTENTION