Feature Space Trajectory Methods for Active Object Recognition

We advance new active object recognition algorithms that classify rigid objects and estimate their pose from intensity images. Our algorithms automatically re-position the sensor if the class or pose of an object is ambiguous in a given image (our algorithms automatically detect ambiguity) and incorporate data from multiple object views in determining the final object classification and pose estimate. A probabilistic feature space trajectory (FST) in a global eigenfeature space is used to represent 3-D distorted views of an object and to estimate the class and pose of an input object. Assuming that an observed feature vector consists of Gaussian noise added to a point on the FST, we derive a probability density function (pdf) for an observation conditioned on the class and pose of the object; we use Bayesian estimation and hypothesis testing theory to show that FST estimates are approximations to the maximum a posteriori probability pose estimate and the minimum probability of error cl..