Robust feature evaluation for multisensory computer vision

Presents a statistically robust approach for multisensory computer vision. Specifically energy exchange model parameters used to inter-relate thermal and visual imagery are reliably computed. The approach extracts physically meaningful estimates of internal object properties which are useful for automated object recognition. The robust technique minimizes sensitivity to outliers caused by segmentation errors and misregistration which are endemic to multisensor fusion