Abstract
This work presents a multiscale image approach for contrast enhancement and segmentation based on a composition of contrast operators. The contrast operators are built by means of the opening and closing by reconstruction. The operator that works on bright regions uses the opening and the identity as primitives, while the one working on the dark zones uses the closing and the identity as primitives. To select the primitives, a contrast criterion given by the connected tophat transformation is proposed. This choice enables us to introduce a well-defined contrast in the output image. By applying these operators by composition according to the scale parameter, the output image not only preserves a well-defined contrast at each scale, but also increases the contrast at finer scales. Because of the use of connected transformations to build these operators, the principal edges of the input image are preserved and enhanced in the output image. Finally, these operators are improved by applying an anamorphosis to the regions verifying the criterion.
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References
Toet, A.: Hierarchical image fusion. Machine Vision and Applications 3, 1–11 (1990)
Mukhopadhyay, S., Chanda, B.: Multiscale morphological approach to local contrast enhancement. Signal Process. 80(4), 685–696 (2000)
Schavemaker, J.G.M., Reinders, M.J.T., Gerbrands, J.J., Backer, E.: Image sharpening by morphological filters. Pattern Recognition 33, 997–1012 (2000)
Potjer, F.K.: Region adyacency graphs and connected morphological operators. In: Maragos, P. (ed.) Mathematical Morphology and Its Applications to Image and Signal Processing, pp. 111–118. Kluwer, Dordrecht (1996)
Terol-Villalobos, I.R., Cruz-Mandujano, J.A.: Contrast enhancement and image segmentation using a class of morphological nonicreasing filters. Journal Electronic Imaging 7, 641–654 (1998)
Meyer, F., Serra, J.: Contrast and activity lattice. Signal Process. 16(4), 303–317 (1989)
Kramer, H.P., Bruckner, J.B.: Iteration of non-linear transformations for enhancement of digital image. Pattern Recognition 7(4), 53–58 (1975)
Terol-Villalobos, I.R.: Nonincreasing filters using morphological gradient criteria, Opt. Engineering 35, 3172–3182 (1996)
Terol-Villalobos, I.R.: Morphological image enhancement and segmentation. In: Hawkes, P.W. (ed.) Advances in Imaging and Electron Physics, vol. 118, pp. 207–273. Academic Press, London (2001)
Serra, J.: Image analysis and mathematical morphology, vol. 2. Academic Press, London (1988)
Vincent, L.: Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Transactions on Image Processing 2, 176–201 (1993)
Meyer, F., Maragos, P.: Nonlinear scale-space representation with morphological levelings. J. Vis. Comm. Image Represent. 11(3), 245–265 (2000)
Meyer, F., Beucher, S.: Morphological segmentation. J. Vis. Comm. Image Represent. 1, 21–46 (1990)
Jalba, A.C., Roerdink, J.B.T.M., Wilkinson, M.H.F.: Morphological hat-transform scale spaces and their use in testure classification. In: Proc. Int. Conf. Image Proc, Barcelona, Spain, vol. I, pp. 329–332 (2003)
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Terol-Villalobos, I.R. (2004). Multiscale Image Enhancement and Segmentation Based on Morphological Connected Contrast Mappings. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_68
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DOI: https://doi.org/10.1007/978-3-540-24694-7_68
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