Adaptive Object Acquisition

We propose an active vision system for object acquisition. The core of our approach is a reinforcement learning module which learns a strategy to scan an object. The agent moves a virtual camera around an object and is able to adapt dynamically to different conditions of its environment such as different objects and different purposes of the data acquisition by means of a reinforcement signal which rewards a chosen action with respect to the intendet purpose. The purpose of the acquisition we consider here is the reconstruction of non-acquired views. The learned scan path allows the generation of a sparse, view-based object representation which consists of some key views of the scan path. We present preliminary results from a project conducted with undergraduate students and show that the scan pattern obtained with the proposed method allows a better reconstruction of unfamiliar views than random scan paths. As the reward signal is based on local information at the current position of...

• Type: Conference
• Year: 2005
• Where: ARCS
• Authors: Gabriele Peters, Claus-Peter Alberts, Markus Briese, Sebastian Entian, Christian Gabriel, Zhiqiang Gao, Alexander Klandt, Thomas Leopold, Jan Schultze, Jeremias Spiegel, Jürgen Thyen, Martina Vaupel, Peter Voß, Qing Zhu
• Keywords: ARCS 2005, Data Acquisition, Object Representations, Scan Paths