Connected operators for unsupervised image segmentation

Abstract

Image segmentation forms the first stage in many image analysis procedures including image sequence re-timing and the emerging field of content based retrieval. By dividing the image into a set of disjoint connected regions, each of which is homogeneous with respect to some measure of the image content, the scene can be analysed and metadata extracted more efficiently, and in many cases more effectively, than on a pixel by pixel basis. Though a great number of segmentation techniques exist (and continue to be developed,) many of them fall short of the requirements of these applications. This thesis first defines these requirements and reviews established segmentation methods describing their qualities and shortfalls. Selecting the watershed transform and connected operators from those techniques reviewed a number of novel adaptations are introduced, developed and shown to produce pleasing results both in terms of a new evaluation metric and subjective appraisal. Finally, the use of the image segmentation is shown to improve established methods of image noise removal using the discrete wavelet transform.

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Identifiers

ORCID for Paul White: orcid.org/0000-0002-4787-8713

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