Abstract
This paper presents a novel object segmentation technique to extract objects that are potentially scattered or distributed over the whole image. The goal of the proposed approach is to achieve accurate segmentation with minimum and easy user assistance. The user provides input in the form of few mouse clicks on the target object which are used to characterize its statistical properties using Gaussian mixture model. This model determines the primary segmentation of the object which is refined by performing morphological operations to reduce the false positives. We observe that the boundary pixels of the target object are potentially misclassified. To obtain an accurate segmentation, we recast our objective as a graph partitioning problem which is solved using the graph cut technique. The proposed technique is tested on several images to segment various types of distributed objects e.g. fences, railings, flowers. We also show some remote sensing application examples, i.e. segmentation of roads, rivers, etc. from aerial images. The obtained results show the effectiveness of the proposed technique.
M. Shahid—The major part of this research was done when the author was associated with institute.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adobe Photoshop: Lasso tool. http://helpx.adobe.com/photoshop/using/selecting-lasso-tools.html
Berman, A., Dadourian, A., Vlahos, P.: Method for removing from an image the background surrounding a selected object. US Patent 6,134,346, 17 October 2000
Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. Mathematical morphology in image processing. Opt. Eng. 34, 433–481 (1993)
Bouman, C.: Cluster: An unsupervised algorithm for modeling Gaussian mixtures, April 1997. http://engineering.purdue.edu/bouman
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)
Boykov, Y., Veksler, O.: Graph Cuts in Vision and Graphics: Theories and Applications. In: Handbook of Mathematical Models in Computer Vision, pp. 79–96. Springer, US (2006)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. Series B (Methodological) 39, 1–38 (1977)
Farid, M.S., Mahmood, A., Grangetto, M.: Image de-fencing framework with hybrid inpainting algorithm. Sig. Image Video Process 10(7), 1193–1201 (2016). http://dx.doi.org/10.1007/s11760-016-0876-7
Juan, O., Boykov, Y.: Active graph cuts. In: Proceedings of the IEEE Computer Society Conference Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 1023–1029, June 2006
Kang, H.: G-wire: a livewire segmentation algorithm based on a generalized graph formulation. Pattern Recognit. Lett. 26(13), 2042–2051 (2005)
Kang, H., Shin, S.: Enhanced lane: interactive image segmentation by incremental path map construction. Graph. Models 64(5), 282–303 (2002)
Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)
Kuntimad, G., Ranganath, H.: Perfect image segmentation using pulse coupled neural networks. IEEE Trans. Neural Netw. 10(3), 591–598 (1999)
Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy snapping. ACM Trans. Graph. 23(3), 303–308 (2004)
Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley Series in Probability and Statistics, 1st edn. Wiley, New York (1987)
Ma, W.Y., Manjunath, B.: EdgeFlow: a technique for boundary detection and image segmentation. IEEE Trans. Image Process. 9(8), 1375–1388 (2000)
Mahalanobis, P.C.: On the generalised distance in statistics. Proc. Nat. Inst. Sci. (India) 2(1), 49–55 (1936)
Mnih, V.: Machine learning for aerial image labeling. Ph.D. thesis, University of Toronto (2013)
Mortensen, E., Morse, B., Barrett, W., Udupa, J.: Adaptive boundary detection using ‘live-wire’ two-dimensional dynamic programming. In: Proceedings of Computers Cardiology, pp. 635–638, October 1992
Mortensen, E., Barrett, W.: Intelligent scissors for image composition. In: Proceedings of the SIGGRAPH, pp. 191–198 (1995)
Mortensen, E., Barrett, W.: Toboggan-based intelligent scissors with a four-parameter edge model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR), vol. 2, pp. 452–458 (1999)
Mubasher, M.M., Farid, M.S., Khaliq, A., Yousaf, M.M.: A parallel algorithm for change detection. In: 15th International Multitopic Conference (INMIC), pp. 201–208, December 2012
Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)
Peng, B., Zhang, L., Zhang, D.: A survey of graph theoretical approaches to image segmentation. Pattern Recogn. 46(3), 1020–1038 (2013). http://www.sciencedirect.com/science/article/pii/S0031320312004219
Peng, B., Zhang, L., Zhang, D., Yang, J.: Image segmentation by iterated region merging with localized graph cuts. Pattern Recogn. 44(10–11), 2527–2538 (2011)
Rissanen, J.: A universal prior for integers and estimation by minimum description length. Ann. Stat. 11(2), 416–431 (1983)
Rother, C., Kolmogorov, V., Blake, A.: “GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)
Ruzon, M., Tomasi, C.: Alpha estimation in natural images. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 18–25 (2000)
Vezhnevets, V., Konouchine, V.: “GrowCut”: Interactive multi-label ND image segmentation by cellular automata. In: Proceedings of Graphicon, pp. 150–156 (2005)
Vicente, S., Kolmogorov, V., Rother, C.: Graph cut based image segmentation with connectivity priors. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8, June 2008
Yang, Q., et al.: Progressive cut: an image cutout algorithm that models user intentions. IEEE Multimedia 14(3), 56–66 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Farid, M.S., Lucenteforte, M., Khan, M.H., Grangetto, M. (2018). Semi-automatic Segmentation of Scattered and Distributed Objects. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-59162-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59161-2
Online ISBN: 978-3-319-59162-9
eBook Packages: EngineeringEngineering (R0)