Mathematical morphology, developed in the early 1960's for
single-component signals, has been applied to a number of image
processing applications. This investigation examines the systematic
extension of mathematical morphology to multi-variate signals.
Two approaches are considered. The first approach, the
extension of the theory of single-component morphological filters to
multi-variate case, fails for reason of the lack of ordering within signal
range space. Therefore, as a second approach, a two stage processing
technique was proposed, consisting of the maximum separation of the
object from its background feature and separate morphological
filtering of each component. To separate the object from its
background, a mapping technique, based upon the normalization and
simultaneous diagonalization of sample covariance matrices (NADCVM),
was applied. Sample variance morphological measure
interpretation demonstrated that NAD-CVM mapping constitutes an
excellent preprocessing tool for morphological filtering of multivariate
signals. An unsupervised NAD-CVM implementation and a
morphological edge detector were tested experimentally to verify the
properties of the theoretical algorithm. In addition, a method for the
application of the proposed method to the analysis of color images was
presented. / Graduation date: 1992
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/36261 |
| Date | 24 February 1992 |
| Creators | Eo, Jin-woo |
| Contributors | Kolodziej, W.J. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.012 seconds