Morphological multiscale image segmentation

Publisher Website
Google Scholar
Abstract
This paper deals with a morphological approach to the problem of unsupervised image segmentation. The proposed technique relies on a multiscale approach which allows a hierarchical processing of the data ranging from the most global scale to the most detailed one. At each scale, the algorithm relies on four steps: preprocessing, feature extraction, decision and quality estimation. The goal of the preprocessing step is to simplify the original signal which is too complex to be processed at once. Morphological filters by reconstruction are very attractive for this purpose because they simplify without corrupting the contour information. The feature extraction intends to extract the pertinent parameters for assessing the degree of homogeneity of the regions. To this goal, morphological techniques extracting flat or contrasted regions are very efficient. The decision step defines precisely the contours of the regions. This decision is achieved by a watershed algorithm. Finally, the quality estimation is used to compute the information that has to be further processed by the next scale to improve the segmentation result. The estimation is based on a region modeling procedure. The resulting segmentation is very robust and can deal with very different types of images. Moreover, the first levels give segmentation results with a few regions; but precisely located contours.

This publication has 0 references indexed in Scilit: