A microscopic study of by a small bridge. The choice of the proper strel is important. Mathematical Morphology is a way of treating images without altering the shape of the objects in the images. It also provides a basis for the theory of the pattern spectrum which gives a histogram of the distribution of the sizes of various objects comprising an image. The parameters dis- tilled from this spectrum allow us to perform a quantitative analysis of the wear of the sliding bearing.
Schematic example of the basic morphological operators. There is also a computational cost involved when pro- Solid line: original object; Dashed line: result object; Circle: cessing an image. Our goal is to implement algorithms structuring element.
Left: dilation; Right: erosion. Initially we concentrate on gray scale images. Other operations, like the opening an erosion followed by a dilation and the closing a dilation followed by an erosion , are derived from the basic operators. If we take a strel and perform an opening on an image, some elements will disappear. If we take a bigger strel, then more ele- ments in the image will vanish.
There is an analogy with the Fourier Fig. A cross- late to the global features of the image or the smooth ob- like structuring element is used. Similarly, big structuring elements in the pattern spectrum preserve the global features of the image A. Ledda and W. The pattern spectrum norm. Some algorithms can only be used with linear strels or compositions of linear strels. Others only work with binary images.
Which algorithm suits our needs best is currently under investigation.
Mathematical Morphology in Image Processing
A fast, gray scale com- patible and relatively easy to implement algorithm with freedom of choice of the structuring element, is the ideal one. Applications and Future Work The main reason for investigating the use and useful- ness of the pattern spectrum is the quantitative analysis in material science. In collaboration with the department of Mechanical Construction and Production, microscopic images of the wear of composite sliding bearing materials will be examined.
The residues after opening the image with increasing struc- ble. In , fractal dimen- From the pattern spectrum we can extract parameters sions in relation to wear mechanisms are investigated.
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With these parameters we have statistical informa-  R. Haralick, L. Shapiro, Computer and Robot Vision, vol. Image Analysis and Processing, Venice, Italy, , pp. Moreover, with increasing n, the net. Coster, D.
Image Processing and Mathematical Morphology: Fundamentals and Applications - CRC Press Book
On the Implementation of Morphological Operations. One Pixel Thick Skeletons. Fast Grayscale Granulometry Algorithms. Implementation of a Distributed Watershed Algorithm.
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Alina N. Moga, Timo Viero, Bogdan P. Dobrin, Moncef Gabbouj. Roerdink, G. Morphogenesis Simulations with Lattice Gas. Gratin, J. Moreso, D. Marshall, G. Matsopoulos, J. Casas, P. Esteban, A. Moreno, M. Joshi, A. Back Matter Pages About this book Introduction Mathematical morphology MM is a theory for the analysis of spatial structures. It is called morphology since it aims at analysing the shape and form of objects, and it is mathematical in the sense that the analysis is based on set theory, topology, lattice algebra, random functions, etc.
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MM is not only a theory , but also a powerful image analysis technique. The purpose of the present book is to provide the image analysis community with a snapshot of current theoretical and applied developments of MM. The book consists of forty-five contributions classified by subject. It demonstrates a wide range of topics suited to the morphological approach.