DocumentCode
1201889
Title
The morphological structure of images: the differential equations of morphological scale-space
Author
Van Den Boomgaard, Rein ; Smeulders, Arnold
Author_Institution
Fac. of Math. & Comput. Sci., Amsterdam Univ., Netherlands
Volume
16
Issue
11
fYear
1994
fDate
11/1/1994 12:00:00 AM
Firstpage
1101
Lastpage
1113
Abstract
We introduce a class of nonlinear differential equations that are solved using morphological operations. The erosion and dilation act as morphological propagators propagating the initial condition into the “scale-space”, much like the Gaussian convolution is the propagator for the linear diffusion equation. The analysis starts in the set domain, resulting in the description of erosions and dilations in terms of contour propagation. We show that the structuring elements to be used must have the property that at each point of the contour there is a well-defined and unique normal vector. Then given the normal at a point of the dilated contour we can find the corresponding point (point-of-contact) on the original contour. In some situations we can even link the normal of the dilated contour with the normal in the point-of-contact of the original contour. The results of the set domain are then generalized to grey value images. The role of the normal is replaced with the function gradient. The same analysis also holds for the erosion. Using a family of increasingly larger structuring functions we are then able to link infinitesimal changes in grey value with the gradient in the image
Keywords
computational geometry; image processing; mathematical morphology; nonlinear differential equations; contour propagation; dilations; erosions; function gradient; grey value images; morphological scale-space; morphological structure; nonlinear differential equations; set domain; Computer vision; Convolution; Differential equations; Morphological operations; Morphology; Nonlinear equations; Partial differential equations; Terminology; Transforms; Vectors;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.334389
Filename
334389
Link To Document