Learning an Analysis Strategy for Knowledge-Based Exploration of Scenes

Abstract

A system is presented which searches with an active camera for known objects, constrained to lie on a table, in an otherwise unknown office using color images. Both camera actions and image processing methods are represented as concepts of a semantic network. Image processing methods comprise depth computation to find a table, generation of object hypotheses in an overview image, and object verification in a close-up view. Camera actions are pan, tilt, zoom, and motion on a linear sledge. System actions, either image processing or camera actions, are initialized by a graph search based control algorithm which tries to compute the best scoring instance of a goal concept. The sequence of actions for computing an instance is determined by precedences which are either adjusted manually or computed from reinforcement-learning. Results are presented comparing the two approaches.

This work was partially supported by the “Deutsche Forschungsgemeinschaft (DFG)” under grant NI 191/12–1. Only the authors are responsible for the content.