Image Sequence Processing For Target Estimation In Forward-Looking Infrared Imagery

Abstract

This paper presents a methodology for estimating the silhouette of a target from a time sequence of forward-looking infrared (FLIR) imagery recorded at video rates. The major problems associated with this type of imagery are noise and frame-to-frame misregistration. We achieve registration and noise smoothing by a combination of spatial and temporal processing. Edge-preserving smoothing is applied to individual frames prior to gray level thresholding. A new algorithm for finding the optimum threshold is presented. The resulting sequence of binary image frames is registered by cross-correlating the 1-D x and y projections of each frame. The computationally efficient 1 -D registration technique is just as accurate as a much slower 2-D correlation method. Temporal smoothing is realized by using a binary median filter to process frame sequences. We show that binary temporal median filtering is comparable in performance with gray level median processing followed by thresholding. Our target estimation approach is applicable in situations in which a single target exists against a background of relatively uncorrelated noise.