Using Elimination Methods to Compute Thermophysical Algebraic Invariants from Infrared Imagery

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Perception Vision

We describe a new approach for computing invariant features in infrared (IR) images. Our approach is unique in the field since it considers not just surface reflection and surface geometry in the specification of invariant features, but it also takes into account internal object composition and thermal state which affect images sensed in the non-visible spectrum. We first establish a non-linear energy balance equation using the principle of conservation of energy at the surface of the imaged object. We then derive features that depend only on material parameters of the object and the sensed radiosity. These features are independent of the scene conditions and the scene-to-scene transformation of the "driving conditions" such as ambient temperature, and wind speed. The algorithm for deriving the invariant features is based on the algebraic elimination of the transformation parameters from the non-linear relationships. The elimination approach is a general method based on the extended Dixon resultant. Results on real IR imagery are shown to illustrate the performance of the features derived in this manner when used for an object recognition system that deals with multiple classes of objects.