Semi-Automatic Probabilistic Morphological Detection
F.M. Coetzee, Visvanathan Ramesh · 2006
We describe a semi-automated approach for designing morphological operators to detect image structures. We automatically combine morphological primitives from a pool to generate an enumerable family of complex morphological operators spanning a receiver operating curve (ROC). Domain knowledge can be encoded by biasing the pool with primitives; the system subsequently automatically selects and combines operators based on the joint statistics of training data. The major advantages are that the designer can focus on constructing simple operators, yet is able to rapidly combine them to yield more powerful, system-specific solutions, whose operating point can easily be changed. We illustrate the approach using birth and death processes and associated operators. Examples of video text detection are presented.