Cost Integration in Multi-step Viewpoint Selection for Object Recognition

During the last years, computer vision tasks like object recognition and localization were rapidly expanded from passive solution approaches to active ones, that is to execute a viewpoint selection algorithm in order to acquire just the most significant views of an arbitrary object. Although fusion of multiple views can already be done reliably, planning is still limited to gathering the next best view, normally the one providing the highest immediate gain in information. In this paper, we show how to perform a generally more intelligent, long-run optimized sequence of actions by linking them with costs. Therefore it will be introduced how to acquire the cost of an appropriate dimensionality in a nonempirical way while still leaving the determination of the system’s basic behavior to the user. Since this planning process is accomplished by an underlying machine learning technique, we also point out the ease of adjusting these to the expanded task and show why to use a multi-step approach for doing so.