IMAGE RE-SEGMENTATION A new approach applied to Urban Imagery Thales Sehn Korting Leila Maria Garcia Fonseca Luciano Vieira Dutra Felipe Castro da Silva.
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IMAGE RE-SEGMENTATIONA new approach applied to Urban Imagery
Thales Sehn KortingLeila Maria Garcia Fonseca
Luciano Vieira DutraFelipe Castro da Silva
Introduction
• Image segmentation is the identification of homogeneous regions in the image
Traditional approaches
• Few versions available for end users
• Algorithms don’t consider the application context– Urban– Agriculture– etc
Urban Segmentation
Objective
• Development of a re-segmentation system, based on rectangular shapes
• Re-Segmentation– Rearrangement of a polygon set, merging
some elements to generate objects with particular characteristics, applied to a specific context
Re-Segmentation Approach
Band 1
Band 2
Band ...Band ...Band ...Band n
Input
Output
Ove
r-S
egm
enta
tion
Segmentation Based on Graph
• Region Adjacency Graph
Re-Segmentation Diagram
Finding Rectangles
Pi
x
y
AV
ANG(Pi)Ri
BOX(Ri)
AREA(Pi)AREA(BOX(Ri))
RET(Pi) =
Results
• Original image• Rectangular shapes highlighted• Classified regions• Resultant polygons
• Hardware– AMD Athlontm 3000+– 512MB RAM– Linux Mandriva 2006
1st – Original
Over-Segmentation
1998 polygons
Classification
trees
roofs
buildings
streets
others
Resultant Re-Segmentation
634 polygons
317 seconds
2nd – Original
Over-Segmentation
2028 polygons
Classification
trees
roofs
buildings
streets
others
Resultant Re-Segmentation
695 polygons
243 seconds
3rd – Original
Over-Segmentation
2264 polygons
Classification
trees
roofs
buildings
streets
others
Resultant Re-Segmentation
750 polygons
196 seconds
Drawbacks
Conclusions
• New approach for image re-segmentation
• Algorithm developed using the Free C++ Library TerraLib (http://www.terralib.org/)
IMAGE RE-SEGMENTATIONA new approach applied to Urban Imagery
END
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