Abstract
Object segmentation is a crucial task for image analysis and has been studied widely in the past. Most segmentation algorithms rely on changes in contrast or on clustering the same colors only. Yet there seem to be no real one-and-for-all solution to the problem. Nevertheless graph-based energy minimization techniques have been proven to yield very good results in comparison to other techniques. They combine contrast and color information into an energy minimization criterion. We give a brief overview of two recently proposed techniques and present some enhancements to them. Furthermore a combination of them into the GrayCut algorithm leads to suitable results for segmenting objects in infrared images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mortensen, E., Barrett, W.: Intelligent scissors for image composition. In: ACM Siggraph, pp. 191–198 (1995)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. In: International Conference on Computer Vision, pp. 259–268 (1987)
Friedland, G., Jantz, K., Rojas, R.: Siox: Simple interactive object extraction in still images. In: International Symposium on Multimedia, pp. 253–259 (2005)
Wippig, D., Klauer, B., Zeidler, H.: Extraction of ship silhouettes using active contours from infrared images. In: International Conference on Computer Vision, pp. 172–177 (2005)
Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: International Conference on Computer Vision, vol. I, pp. 105–112 (2001)
Rother, C., Kolmogorov, V., Blake, A.: Grabcut - interactive foreground extraction using iterated graph cuts. In: Proc. ACM Siggraph, pp. 309–314 (2004)
Rusch, O., Ruwwe, C., Zoelzer, U.: Image segmentation in naval ship images. In: 11. Workshop Farbbildverarbeitung, pp. 63–70 (2005)
Greig, D., Porteous, B., Seheult, A.: Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society Series B 51(2), 271–279 (1989)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 26(2), 147–159 (2004)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society Series B 39(1), 1–38 (1977)
Wippig, D., Klauer, B., Zeidler, H.: Denoising of infrared images by wavelet thresholding. In: International Conference on Industrial Electronics, Technology & Automation (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ruwwe, C., Zölzer, U. (2006). GrayCut – Object Segmentation in IR-Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_70
Download citation
DOI: https://doi.org/10.1007/11919476_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48628-2
Online ISBN: 978-3-540-48631-2
eBook Packages: Computer ScienceComputer Science (R0)