Active exploration: Knowing when we´re wrong

Many strategies in computer vision assume the existence of general purpose models that can be used to characterize a scene or environment at various levels of abstraction. The usual assumptions are that a selected model is competent to describe a particular attribute and that the parameters of this model can be estimated by interpreting the input data in an appropriate manner. The authors consider the problem of determining when these assumptions break down so that an alternate model may be considered or further interpretation of data performed. Specifically, how this can be accomplished is analyzed within the framework of an approach that actively builds a description of the environment from several different viewpoints. It is shown that a gaze planning strategy used to minimize model parameter variance can also be used to decide whether the model itself provides an adequate description of the environment