New Techniques for Tracking Sequences of Digitized Images.
Abstract
A model for a generalized image tracking system is presented. Characteristics of minimum norm similarity detectors are investigated. A first-order local tangent plane model for digitized imagery is used to successfully predict properties of the auto and cross distance functions for real data. A matrix signal-to-noise ratio is shown to be the natural signal-to-noise ratio for the minimum norm detection problem, and an approximation is derived and experimentally verified for an upper bound on the probability that a minimum norm detector makes a particular error. A non-linear two-dimensional filter is presented which shows a significant reduction in noise variance in low contrast regions of an image. An optimum weighted norm is derived which minimizes the probability of making a registration error, and an adaptive reference set selection algorithm is presented which maximizes the tracking signal-to-noise ratio. The adapitve reference set selection algorithm uses the histogram of gradient magnitudes and includes a new gradient estimator/classifier with a fixed probability of error. An adaptive Kalman filter is developed to update the reference image and the filter is shown to be stable in all areas of interest. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 1978
- Accession Number
- ADA066194
Entities
People
- Llewellyn S. Dougherty
Organizations
- Air Force Institute of Technology