1. IMAGE RE-SEGMENTATION A new approach applied to Urban Imagery Thales Sehn Korting Leila Maria Garcia Fonseca Luciano Vieira Dutra Felipe Castro da Silva

2. Introduction Image segmentation is the identification of homogeneous regions in the image

3. Traditional approaches Few versions available for end users Algorithms dont consider the application context –Urban –Agriculture –etc

4. Urban Segmentation

5. 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

6. Re-Segmentation Approach Band 1 Band 2 Band... Band n Input Output Over- Segmentation

7. Segmentation Based on Graph Region Adjacency Graph

8. Re-Segmentation Diagram

9. Finding Rectangles PiPi x y AV ANG(P i ) RiRi BOX(R i ) AREA(P i ) AREA(BOX(R i )) RET(P i ) =

10. Results Original image Rectangular shapes highlighted Classified regions Resultant polygons Hardware –AMD Athlon tm 3000+ –512MB RAM –Linux Mandriva 2006

11. 1 st – Original

12. Over-Segmentation 1998 polygons

13. Classification trees roofs buildings streets others

14. Resultant Re-Segmentation 634 polygons 317 seconds

15. 2 nd – Original

16. Over-Segmentation 2028 polygons

17. Classification trees roofs buildings streets others

18. Resultant Re-Segmentation 695 polygons 243 seconds

19. 3 rd – Original

20. Over-Segmentation 2264 polygons

21. Classification trees roofs buildings streets others

22. Resultant Re-Segmentation 750 polygons 196 seconds

23. Drawbacks

24. Conclusions New approach for image re-segmentation Algorithm developed using the Free C++ Library TerraLib ( http://www.terralib.org/ )

25. IMAGE RE-SEGMENTATION A new approach applied to Urban Imagery END