By Phillipe Réfrégier, François Goudail
ISBN-10: 030647865X
ISBN-13: 9780306478659
Statistical Processing options for Noisy pictures offers a statistical framework to layout algorithms for objective detection, monitoring, segmentation and class (identification). Its major objective is to supply the reader with effective instruments for constructing algorithms that remedy his/her personal picture processing purposes. specifically, such issues as speculation test-based detection, quick energetic contour segmentation and set of rules layout for non-conventional imaging structures are comprehensively handled, from theoretical foundations to useful implementations. With a wide variety of illustrations and useful examples, this e-book serves as an very good textbook or reference booklet for senior or graduate point classes on statistical signal/image processing, in addition to a reference for researchers in comparable fields.
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Extra resources for Statistical image processing techniques for noisy images: an application-oriented approach
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.