Image re-segmentation - A new approach applied to urban imagery

VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings

Volumn 1, Issue , 2008, Pages 467-472

  Korting, Thales Sehn ^a   Fonseca, Leila Maria Garcia ^a   Dutra, Luciano Vieira ^a   Da Silva, Felipe Castro ^a  

^a INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

Author keywords

Graph based segmentation; Re segmentation; Remote sensing; Urban imagery

Indexed keywords

APPLICATIONS; COMPUTER VISION; IMAGE PROCESSING; IMAGE SEGMENTATION; REMOTE SENSING;

CUTTING OPERATIONS; GRAPH BASED SEGMENTATION; NEW APPROACHES; RE-SEGMENTATION; SEGMENTED IMAGES; URBAN IMAGERY; URBAN OBJECTS;

COMPUTATIONAL GEOMETRY;

EID: 57349152386     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Baatz, M. and Schäpe, A. (2000). Multiresolution Segmentation-an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII, Wichmann-Verlag, Heidelberg, 12:12-23.
2. Bins, L., Fonseca, L., Erthal, G., and Ii, F. (1996). Satellite imagery segmentation: a Region Growing ap proach. Simpósio Brasileiro de Sensoriamento Remoto, 8:677-680.
3. Câmara, G., Souza, R., Freitas, U., and Garrido, J. (1996). Spring: integrating remote sensing and GIS by object-oriented data modelling. Computers & Graphics, 20(3):395-403.
4. Câmara, G., Souza, R., Pedrosa, B., Vinhas, L., Monteiro, A., Paiva, J., Carvalho, M., and Gatass, M. (2000). TerraLib: Technology in Support of GIS Innovation. Workshop Brasileiro de Geoinformática, GeoInfo2000, 2: 1-8.
5. Chen, Z., Zhao, Z., Gong, P., and Zeng, B. (2006). A new process for the segmentation of high resolution remote sensing imagery. International Journal of Remote Sensing, 27(22):4991-5001.
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. Falcão, A. X., Stolfi, J., and Lotufo, R. A. (2004). The Image Foresting Transform: Theory, Algorithms, and Applications. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26(1): 19-29.
9. Felzenszwalb, P. and Huttenlocher, D. (2004). Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2): 167-181.
10. Hart, P., Nilsson, N., and Raphael, B. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. Systems Science and Cybernetics, IEEE Transactions on, 4(2): 100-107.
11. Kohonen, T. (2001). Self-Organizing Maps. Springer.
12. Lezoray, O., Elmoataz, A., SRC, I., EA, L., and Saint-Lo, F. (2003). Graph based smoothing and segmentation of color images. Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on, 1.
13. Lucchese, L. and Mitra, S. (2001). Color image segmentation: A state-of-the-art survey. Proc. of the Indian National Science Academy (INSA-A), 67(2):207-221.
14. Schettini, R. (1993). A segmentation algorithm for color images. Pattern Recogn. Lett., 14(6):499-506.
15. Shi, J. and Malik, J. (2000). Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905.
16. Tremeau, A. and Colantoni, P. (2000). Regions adjacency graph applied to color image segmentation. Image Processing, IEEE Transactions on, 9(4):735-744.