By John Goutsias, Luc Vincent, Dan S. Bloomberg
ISBN-10: 030647025X
ISBN-13: 9780306470257
ISBN-10: 0792378628
ISBN-13: 9780792378624
Mathematical morphology is a strong technique for the processing and research of geometric constitution in signs and photographs. This ebook comprises the complaints of the 5th overseas Symposium on Mathematical Morphology and its purposes to photograph and SignalProcessing, held June 26-28, 2000, at Xerox PARC, Palo Alto, California. It offers a large sampling of the latest theoretical and useful advancements of mathematical morphology and its functions to photo and sign processing. components lined comprise: decomposition of structuring services and morphological operators, morphological discretization, filtering, connectivity and hooked up operators, morphological form research and interpolation, texture research, morphological segmentation, morphological multiresolution ideas and scale-spaces, and morphological algorithms and purposes.
Audience: the subject material of this quantity might be of curiosity to electric engineers, computing device scientists, and mathematicians whose examine paintings is targeted at the theoretical and sensible points of nonlinear sign and snapshot processing. it's going to even be of curiosity to these operating in machine imaginative and prescient, utilized arithmetic, and machine graphics.
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Extra info for Mathematical Morphology and its Applications to Image and Signal Processing
Sample text
In this decomposition u always has a finite effective domain, while r only has a finite effective domain if ƒ has a finite effective domain. To simplify the decomposition, we assume that ƒ(x) < 0, except for x = 0, where ƒ(0) = 0. For proper concave functions this comes down to translating the function such that the maximum is obtained in the origin. Since the decomposed function is used for dilation, this only results in a simple translation of the result. The function u is constructed from ƒ as follows (4) where are chosen such that and numbers t 1 ≤ 0, t 2 ≤ 0 and constructed from ƒ as follows and for some real see figure 3.
M. Tajine and C. Ronse. Preservation of topology by Hausdorff discretization and comparison to other discretization schemes. Submitted, 1999. 20. M. Tajine and C. Ronse. Hausdorff sampling of closed sets in a boundedly compact space. In preparation, 2000. 21. M. Tajine and C. Ronse. Topological properties of Hausdorff discretizations. Working document in preparation, 2000. 22. M. Tajine, D. Wagner and C. Ronse. Hausdorff discretization and its comparison with other discretization schemes. DGCI’99, Paris, LNCS Springer-Verlag, Vol.
Tajine and C. Ronse. An approach to discretization based on the Hausdorff metric. In H. Heijmans & J. Roerdink, editors, International Symposium on Mathematical Morphology 1998. Mathematical morphology and its applications to image and signal processing IV, pp. 67–74, Kluwer Academic Publishers, June 1998. fr A b s t r a c t . It is possible to level a colour image by applying to each of its coordinates a separate grey-tone leveling. Interpreting this operation in a vectorial space gives a better insight into levelings and flattenings, interpreted as Minkowski subtractions.