Morphological texture-based maximum-likelihood pixel classification based on local granulometric moments

摘要

Morphological granulometries are one-parameter filter sequences that monotonically decrease image area. A size distribution is generated by measuring the residual area after each iteration of the filter sequence. Normalization yields a probability distribution function whose moments can be employed as image signatures. By measuring residual area locally over a window about each point of an image instead of over the entire image, local texture features are generated at each pixel, and these features can be employed for pixel classification. By using several granulometric structuring-element generating sequences, numerous moment sets result, each carrying different textural information. A detailed analysis of this pixel classification methodology using a Gaussian maximum likelihood classifier is provided. Included is a statistical study of classification accuracy, feature optimization, and robustness with respect to various relevant noise models.