Robust physics-based analysis of thermal and visual imagery.

Abstract

Extensions to an existing physics-based approach for intersensory perception in which thermal and visual imagery of outdoor scenes is analyzed simultaneously for object recognition are discussed. The existing approach uses a model that is based on the principle of the conservation of energy at the surface of the imaged object. The model permits the computation of physically meaningful features that may be used for object classification. Two significant extensions are discussed. First, the model is used to analyze a temporal sequence of spatially registered thermal and visual imagery. Second, the energy-exchange model is used to formulate a linear-regression task in which the physical properties of the imaged object are the unknown parameters that are estimated. A statistically robust scheme is presented for this task. The robust technique minimizes sensitivity to outliers caused by segmentation errors and misregistration, which are endemic to multisensor fusion. Thus reliable physics-based features are made available by this approach.