Feedback control strategies for object recognition

We present a paradigm for feedback strategies that find instances of a generic class of objects by improving on established single-pass hypothesis generation and verification approaches. We improve upon the mechanisms of the traditional or classical image processing systems by introducing control strategies at low, intermediate, and high levels of analysis. We produce optimal sets of low-level features to reduce the number of hypotheses generated. The feedback further enables updated sets of features to be extracted so that the target object may be located even in very, noisy data. The use of an interest operator in the feedback directs the search through the hypotheses in an optimal manner, so minimizing the amount of feedback to false alarms. Furthermore, we aim to obtain detailed information about a complex object and not just its location. Thus, following top-down recognition of the object our feedback control directs the search for missing information. The system can extract complex objects in a scale and rotation independent manner where the objects may be partially occluded. The method is illustrated using box shaped objects and noisy IR images of a number of bridges