ROBUST PHYSICS-BASED ANALYSIS OF THERMAL AND VISUAL-IMAGERY

Extensions to an existing physics-based approach for intersensory perc eption in which thermal and visual imagery of outdoor scenes is analyz ed simultaneously for object recognition are discussed. The existing a pproach uses a model that is based on the principle of the conservatio n of energy at the surface of the imaged object. The model permits the computation of physically meaningful features that may be used for ob ject classification. Two significant extensions are discussed. First, the model is used to analyze a temporal sequence of spatially register ed thermal and visual imagery. Second, the energy-exchange model is us ed to formulate a linear-regression task in which the physical propert ies 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 er rors and misregistration, which are endemic to multisensor fusion. Thu s reliable physics-based features are made available by this approach.