Extraction of Geometric Features with Functional Models

In this contribution, we show how to encode in a convenient and compact way information from images with so called functional graphical models. Those models permit to represent in a coherent mathematical framework features such as interest points , simply connected curves such as line and conic segments, connected regions of homogeneous properties, and compound features such as lines and line pencils. We show also the relevance of functional models for feature extraction, which is realized by minimizing a global model selection function. We apply the framework for encoding/extracting geometrical features thanks to two fast sub-optimal algorithms.