Learning Temporal Context in Active Object Recognition Using Bayesian Analysis

Active object recognition is a successful strategy to reduce the uncertainty of single view recognition, by planning sequences of views, actively obtaining these views, and integrating multiple recognition results. Understanding recognition as a sequential decision problem challenges the visual agent to select discriminative information sources. The presented system emphasizes the importance of temporal context in disambiguating initial object hypotheses, provides the corresponding theory for Bayesian fusion processes, and demonstrates its performance to be superior to alternative view planning schemes. Instance based learning proposed to estimate the control function enables then real-time processing with improved performance characteristics.