Morphological hat-transform scale spaces and their use in texture classification

In this paper we present a multi-scale morphological method for use in texture classification. A connected operator similar to the morphological hat-transform is defined, and two scale-space representations are built. The most important features are extracted from the scale spaces by unsupervised cluster analysis, and the resulting pattern vectors provide the input of a decision tree classifier. We obtain 93.5 % correct classification for the Brodatz texture database.

• Type: Conference
• Year: 2003
• Where: ICIP
• Authors: Andrei Jalba, Jos B. T. M. Roerdink, Michael H. F. Wilkinson
• Keywords: Brodatz Texture Database, Decision Tree Classifier, ICIP 2003, Image Processing, Multi-scale Morphological Method, Texture Classification, Unsupervised Cluster Analysis