Abstract: Computer software for and a method of determining location of a target image within a two-dimensional input image by employing a three-dimensional reference image comprising determining object edge points in the input image; given estimated seeker line-of-sight orientation and range data, computing a two-dimensional reference image from the three-dimensional reference image; applying a weighted-direction-cross-product Hough Transform to the object edge points and points of the two-dimensional reference image; classifying the input image as containing a target image or not; if the image contains a target image, identifying a location of the target image within the two-dimensional input image; computing confidence measure as to the selected location of the target image; and in the case of sequential decision, also performing confidence accumulation through multi-frames or multi-looks.

Abstract: An FFT correlation tracker that is capable of effectively tracking targets against non-uniform backgrounds in realtime, includes a background correction implemented using a FFT with the 2-dimension sinc function. The tracker tracks an object by effectively computing the first and third terms of the mean-square-error function C(s,t) defined as C ? ( s , t ) = 1 N ? ? N ? f 2 ? ( x , y ) + 1 N ? ? N ? g 2 ? ( x - s , y - t ) - 2 · 1 N ? ? N ? [ f ? ( x , y ) · g ? ( x - s , y - t ) ] This is done by first transforming the first and third terms into the frequency domain, where the first term, the background correction term, can be computed much more efficiently in real-time by using the 2-dimension sinc function. Multiplications and additions necessary to carry out the computations in the frequency domain are then performed.

Abstract: An FFT correlation tracker that is capable of effectively tracking targets against non-uniform backgrounds in realtime, includes a background correction implemented using a FFT with the 2-dimension sinc function. The tracker tracks an object by effectively computing the first and third terms of the mean-square-error function C(s,t) defined as C ? ( s , t ) = 1 N ? ? N ? f 2 ? ( x , y ) + 1 N ? ? N ? g 2 ? ( x - s , y - t ) - 2 · 1 N ? ? N ? [ f ? ( x , y ) · g ? ( x - s , y - t ) ] This is done by first transforming the first and third terms into the frequency domain, where the first term, the background correction term, can be computed much more efficiently in real-time by using the 2-dimension sinc function. Multiplications and additions necessary to carry out the computations in the frequency domain are then performed.

Abstract: A multi-stage infrared (IR) image-based tracking system is provided that accurately identifies and tracks targets across a variety of different situations and environmental conditions. The system includes primary and secondary trackers. The secondary tracker supports the primary tracker by identifying and tracking targets when the primary tracker loses or fails to identify one or more targets, and helps the primary tracker reacquire valid targets. In an exemplary embodiment of the invention, the primary tracker is a correlation-based tracker, and the secondary tracker is a feature-based tracker. A prescreener is also included, which operates concurrently with the correlation-based and feature-based trackers to generate a list of possible targets. The list can be used to provide a confidence level for the probable targets identified by each of the primary and secondary trackers.

Abstract: Computer software for and a method of determining location of a target image within a two-dimensional input image by employing a three-dimensional reference image comprising determining object edge points in the input image; given estimated seeker line-of-sight orientation and range data, computing a two-dimensional reference image from the three-dimensional reference image; applying a weighted-direction-cross-product Hough Transform to the object edge points and points of the two-dimensional reference image; classifying the input image as containing a target image or not; if the image contains a target image, identifying a location of the target image within the two-dimensional input image; computing confidence measure as to the selected location of the target image; and in the case of sequential decision, also performing confidence accumulation through multi-frames or multi-looks.

Abstract: An FFT correlation tracker that is capable of effectively tracking targets against non-uniform backgrounds in realtime, includes a background correction implemented using a FFT with the 2-dimension sinc function. The tracker tracks an object by effectively computing the first and third terms of the mean-square-error function C(s,t) defined as C ? ( s , t ) = 1 N ? ? N ? ? f 2 ? ( x , y ) + 1 N ? ? N ? ? g 2 ? ( x - s , y - t ) - 2 · 1 N ? ? N ? ? [ f ? ( x , y ) · g ? ( x - s , y - t ) ] This is done by first transforming the first and third terms into the frequency domain, where the first term, the background correction term, can be computed much more efficiently in real-time by using the 2-dimension sinc function. Multiplications and additions necessary to carry out the computations in the frequency domain are then performed.

Abstract: An FFT correlation tracker that is capable of effectively tracking targets against non-uniform backgrounds in realtime, includes a background correction implemented using a FFT with the 2-dimension sinc function. The tracker tracks an object by effectively computing the first and third terms of the mean-square-error function C(s,t) defined as C ? ( s , t ) = ? 1 N ? ? N ? f 2 ? ( x , y ) + 1 N ? ? N ? g 2 ? ( x - s , y - t ) - ? 2 · 1 N ? ? N ? [ f ? ( x , y ) · g ? ( x - s , y - t ) ] This is done by first transforming the first and third terms into the frequency domain, where the first term, the background correction term, can be computed much more efficiently in real-time by using the 2-dimension sinc function. Multiplications and additions necessary to carry out the computations in the frequency domain are then performed.