DETECTING RECTANGULAR OBJECTS IN URBAN IMAGERY - A Re-Segmentation Approach

Thales Sehn Korting, Luciano Vieira Dutra, Leila Maria Garcia Fonseca

2009

Abstract

Image segmentation is a broad area, which covers strategies for splitting one input image into its components. This paper aims to present a re-segmentation approach applied to urban imagery, where the interest elements (houses roofs) are considered to have a rectangular shape. Our technique finds and generates rectangular objects, leaving the remaining objects as background. With an over-segmented image we connect adjacent objects in a graph structure, known as Region Adjacency Graph - RAG. We then go into the graph, searching for best cuts that may result in segments more rectangular, in a relaxation-like approach. Graph search considers information about object class, through a pre-classification stage using Self-Organizing Maps algorithm. Results show that the method was able to find rectangular elements, according user-defined parameters, such as maximum levels of graph searching and minimum degree of rectangularity for interest objects.

References

  1. Benediktsson, J., Pesaresi, M., and Amason, K. (2003). Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. Geoscience and Remote Sensing, IEEE Transactions on, 41(9 Part 1):1940-1949.
  2. Borenstein, E., Sharon, E., and Ullman, S. (2004). Combining Top-Down and Bottom-Up Segmentation. In Computer Vision and Pattern Recognition Workshop, 2004 Conference on, pages 46-46.
  3. Chesnel, A.-L., Binet, R., and Wald, L. (2007). Object oriented assessment of damage due to natural disaster using very high resolution images. Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International, pages 3736-3739.
  4. Cinque, L., De Rosa, F., Lecca, F., and Levialdi, S. (2004). Image retrieval using resegmentation driven by query rectangles. Image and Vision Computing, 22(1):15- 22.
  5. Donnay, J., Barnsley, M., Longley, P., (ESF), E. S. F., and GISDATA. (2001). Remote Sensing and Urban Analysis. Taylor & Francis.
  6. Duarte, A., Sánchez, Á., Fernández, F., and Montemayor, A. (2006). Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic. Pattern Recognition Letters, 27(11):1239-1251.
  7. Egenhofer, M. and Franzosa, R. (1991). Point-set topological spatial relations. International Journal of Geographical Information Science, 5(2):161-174.
  8. Felzenszwalb, P. and Huttenlocher, D. (2004). Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2):167-181.
  9. Feris, R., Krueger, V., and Cesar, R. (2004). A wavelet subspace method for real-time face tracking. RealTime Imaging, 10(6):339-350.
  10. Haralick, R. and Shapiro, L. (1985). Image segmentation techniques. Computer vision, graphics, and image processing, 29(1):100-132.
  11. He, Y., Wang, H., and Zhang, B. (2004). Color-based road detection in urban traffic scenes. Intelligent Transportation Systems, IEEE Transactions on, 5(4):309- 318.
  12. Kohonen, T. (2001). Self-Organizing Maps. Springer.
  13. Korting, T. S., Fonseca, L. M. G., Dutra, L. V., and Silva, F. C. (2008). Image Re-Segmentation - A New Approach Applied to Urban Imagery. pages 467-472.
  14. Leibe, B., Leonardis, A., and Schiele, B. (2004). Combined object categorization and segmentation with an implicit shape model. In Workshop on Statistical Learning in Computer Vision, ECCV, pages 17-32.
  15. Li, S., Jain, A., and service), S. O. (2005). Handbook of Face Recognition. Springer.
  16. Pérez, A., López, F., Benlloch, J., and Christensen, S. (2000). Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture, 25(3):197-212.
  17. Roller, D., Daniilidis, K., and Nagel, H. (1993). Modelbased object tracking in monocular image sequences of road traffic scenes. International Journal of Computer Vision, 10(3):257-281.
  18. Schettini, R. (1993). A segmentation algorithm for color images. Pattern Recognition Letters, 14(6):499-506.
  19. Silva, M., Câmara, G., Souza, R., Valeriano, D., and Escada, M. (2005). Mining Patterns of Change in Remote Sensing Image Databases. In The Fifth IEEE International Conference on Data Mining, New Orleans, Louisiana, USA.
  20. Tremeau, A. and Colantoni, P. (2000). Regions adjacency graph applied to color image segmentation. Image Processing, IEEE Transactions on, 9(4):735-744.
  21. Zahn, C. (1971). Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters. Transactions on Computers, 100(20):68-86.
Download


Paper Citation


in Harvard Style

Sehn Korting T., Vieira Dutra L. and Garcia Fonseca L. (2009). DETECTING RECTANGULAR OBJECTS IN URBAN IMAGERY - A Re-Segmentation Approach . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 231-236. DOI: 10.5220/0001806702310236


in Bibtex Style

@conference{visapp09,
author={Thales Sehn Korting and Luciano Vieira Dutra and Leila Maria Garcia Fonseca},
title={DETECTING RECTANGULAR OBJECTS IN URBAN IMAGERY - A Re-Segmentation Approach},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={231-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001806702310236},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - DETECTING RECTANGULAR OBJECTS IN URBAN IMAGERY - A Re-Segmentation Approach
SN - 978-989-8111-69-2
AU - Sehn Korting T.
AU - Vieira Dutra L.
AU - Garcia Fonseca L.
PY - 2009
SP - 231
EP - 236
DO - 10.5220/0001806702310236