Method and apparatus for detecting innovations in a scene

Details Method and apparatus for detecting innovations in a scene Method and apparatus for detecting innovations in a scene

1988-05-31

1990-06-05

Groody, James J.

Facsimile and static presentation processing

Facsimile

Specific signal processing circuitry

358125, 364521, H04N 718

Patent

active

049318683

ABSTRACT:
A method and apparatus for detecting innovations in a scene in an image of the type having a large array of pixels. The method comprises the step of generating a multitude of parallel signals representing the amount of light incident on a group of adjacent pixels (masks) and these signals may be considered as forming a n by one vector, Z, where n equals the number of pixels in the masks. L such groups of adjacent pixels or elementary masks are used to geometrically cover the entire image in parallel. The method further comprises the step of replicating the generating step a multitude of times to generate a multitude of Z vectors by taking multiple frames of observations of the image (scene). These Z vectors may be represented in the form A.sub.k, where k equals 1,2,3, . . . , m, where m equals the number of replicates. Each of the Z.sub.k vectors are related to a vector .beta..sub.k of three parameters by a measurement equation in a linear model framework, i.e. Z.sub.k =D.beta..sub.k +e.sub.k, where e.sub.k is an additive noise term. In one embodiment, a solution of the linear model yields the best estimates of the parameters .beta..sub.k =D.sup.t Z.sub.k, where D.sup.T is a three by four matrix, .beta..sub.k is a three by one vector, and Z.sub.k is a four by one vector of the measurements. .beta..sub.k includes three components u.sub.k, A.sub.k and .beta..sub.k. The values of u.sub.k, A.sub.k, and B.sub.k are monitored over time, and a signal is generated whenever any one of these variables rises above a respective preset level.

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"Robust Tracking Novelty Filters Based on Linear Models", by Ivan Kadar, Proceedings of the IEEE First Annual International Conference on Neural Networks, Jun. 1987.

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