Image Segmentation and Contour Recognition Based on Mathematical Morphology

Authors: Pinaki Pratim Acharjya, Esha Dutta

Abstract:

In image segmentation contour detection is one of the important pre-processing steps in recent days. Contours characterize boundaries and contour detection is one of the most difficult tasks in image processing. Hence it is a problem of fundamental importance in image processing. Contour detection of an image decreases the volume of data considerably and useless information is removed, but the structural properties of the image remain same. In this research, a robust and effective contour detection technique has been proposed using mathematical morphology. Three different contour detection results are obtained by using morphological dilation and erosion. The comparative analyses of three different results also have been done.

Keywords: Image segmentation, contour detection, mathematical morphology.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316089

References:

[1] Jitendra Malik, “Normalized Cuts and Image Segmentation”, IEEE Trans. Pattern Analysis and Machine Intelligence. Vol 22, No 8, 2000.
[2] Leo Grady, "Random Walks for Image Segmentation", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1768–1783, Vol. 28, No. 11, 2006.
[3] Hossein Mobahi, Shankar Rao, Allen Yang, Shankar Sastry and Yi Ma, “Segmentation of Natural Images by Texture and Boundary Compression”, International Journal of Computer Vision (IJCV), 95 (1), pg. 86-98, Oct. 2011.
[4] Koenderink, Jan "The structure of images", Biological Cybernetics, 50:363–370, 1984.
[5] L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 6, pp. 583-598, Jun. 1991.
[6] S. Beucher, “Watersheds of functions and picture segmentation,” in Proc. IEEE Int. Conf. Acoustic, Speech, Signal Processing, pp. 1982-`931, 1982..
[7] S. Beucher and C. Lantuejoul, "Use of watersheds in contour detection," 1979.
[8] F. Meyer, "Topographic distance and watershed lines," Signal Processing, vol. 38, pp. 113-125, 1994.
[9] Rafael C. Gonzalez, Richard E Woods. Digital Image Processing. Prentice Hall, second edition, 2002.
[10] Milan Sonka et. al. Image Processing, Analysis and Machine Vision. PWS Publishing, second edition, 1999.
[11] Pierre Soille, Morphological Image Analysis. Springer-Verlag, 2003.
[12] J. Crespo, J. Serra, R. W. Schafer, “Theoretical aspects of morphological filters by reconstruction”, Signal Processing, Vol. 47, No 2, pp. 201-225, 1995.
[13] H.J.A.M. Heijmans, “Connected Morphological operators for binary images”, Computer Vision and Image Understanding, Vol. 73, pp. 99-120, 1999.
[14] C. Ronse, “Set theoretical algebraic approaches to connectivity in continuous or digital spaces”, Journal of Mathematical Imaging and Vision, Vol. 8, No.1, pp. 41-58, 1998.