Abstract
Many image segmentation techniques are available in the literature. One of the most popular techniques is region growing. Research on region growing, however, has focused primarily on the design of feature extraction and on growing and merging criterion. Most of these methods have an inherent dependence on the order in which the points and regions are examined. This weakness implies that a desired segmented result is sensitive to the selection of the initial growing points and prone to over-segmentation. This paper presents a novel framework for avoiding anomalies like over-segmentation. In this article, we have proposed an edge preserving segmentation technique for segmenting aerial images. The approach implicates the preservation of edges prior to segmentation of images, thereby detecting even the feeble discontinuities. The proposed scheme is tested on two challenging aerial images. Its effectiveness is provided by comparing its results with those of the state-of-the-art techniques and the results are found to be better.
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References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Pearson Education, Singapore (2001)
Pun, T.: Entropic thresholding. A new approach. Comput. Graph. Image Process. 16, 210–236 (1981)
Peak, J., Tag, P.: Segmentation of satellite weather imagery using hierarchical thresholding and neural networks. J. Appl. Meteorol. 33, 605–616 (1994)
Ghosh, A., Subudhi, B.N., Ghosh, S.: Object detection from videos captured by moving camera by fuzzy edge incorporated markov random field and local histogram matching. IEEE Trans. Circuits Syst. Video Technol. 22(8), 1127–1135 (2012)
Berthod, M., Kato, Z., Yu, S., Zerubia, J.: Bayesian image classification using markov random fields. Image Vis. Comput. 14, 285–295 (1996)
Zucker, S.W.: Region growing: childhood and adolescence. Comput. Graph. Image Process. 5, 382–399 (1976)
Kim, B.-G., Shim, J.-I., Park, T.-J.: Unsupervised video object segmentation and tracking based on new edge features. Pattern Recogn. Lett. 25, 1731–1742 (2004)
Hu, X., Tao, C.V., Prenzel, B.: Automatic segmentation of high-resolution satellite imagery by integrating texture, intensity, and color features. Photogram. Eng. Remote Sens. 71(12), 1399–1406 (2005)
Hojjatoleslami, S.A., Kittler, J.: Region growing: a new approach. IEEE Trans. Image Process. 7, 1079–1084 (1998)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
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Subudhi, B.N., Patwa, I., Ghosh, A., Cho, SB. (2015). Edge Preserving Region Growing for Aerial Color Image Segmentation. In: Jain, L., Patnaik, S., Ichalkaranje, N. (eds) Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 309. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2009-1_54
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DOI: https://doi.org/10.1007/978-81-322-2009-1_54
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