Granulometric features have been widely used for classification, segmentation and recently in estimation of parameters in shape models. In this paper we study the inference of clustering based on granulometric features for a collection of structuring probes in the context of random models. We use random Boolean models to represent grains of different shapes and structure. It is known that granulometric features are excellent descriptors Of shape and structure of grains. Inference based on clustering these features helps to analyze the consistency of these features and clustering algorithms. This greatly aids in classifier design and feature selection. Features and the order of their addition play a role in reducing the inference errors. We study four different types of feature addition methods and the effect of replication in reducing the inference errors.
Image Processing: Algorithms and Systems
IMAGE PROCESSING: ALGORITHMS AND SYSTEMS
- Random Models
- Clustering Inference
- Granulometries
- Mathematical Morphology
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