Abstract: Methods for detecting areas of interest in an image using combined edge magnitude and edge direction analysis techniques are presented. One embodiment features using thermal imaging data to detect hotspots in maritime settings that may be potential targets for tracking or weapons systems. The edge magnitude and edge direction data are derived from the intensity image and then combined with the intensity image and analyzed morphologically to remove noise and background elements. The combined image data is then selectively filtered to remove horizontal non-target elements and then analyzed further against target size information to determine which detected and analyzed hotspots are valid targets. Another embodiment features receiving as input an intensity image along with its associated edge magnitude and edge direction images, which have both been created by a means outside the detection method. Yet another embodiment features a detection method that does not selectively filter out horizontal image elements.

Abstract: A method and apparatus for tracking an object in an image. An image of a scene comprising an object is received. The image is processed with a first tracker to produce a first estimate of a position of the object in the image and a first confidence value. The image is processed with a second tracker to produce a second estimate of the position of the object in the image and a second confidence value. The second tracker processes the image concurrently with the first tracker. An estimate of the position of the object in the image is selected using the first and second confidence values. The object is then tracked using the selected estimate.

Abstract: Methods for detecting areas of interest in an image using combined edge magnitude and edge direction analysis techniques are presented. One embodiment features using thermal imaging data to detect hotspots in maritime settings that may be potential targets for tracking or weapons systems. The edge magnitude and edge direction data are derived from the intensity image and then combined with the intensity image and analyzed morphologically to remove noise and background elements. The combined image data is then selectively filtered to remove horizontal non-target elements and then analyzed further against target size information to determine which detected and analyzed hotspots are valid targets. Another embodiment features receiving as input an intensity image along with its associated edge magnitude and edge direction images, which have both been created by a means outside the detection method. Yet another embodiment features a detection method that does not selectively filter out horizontal image elements.

Abstract: A method and system detects candidate targets or objects from a viewed scene by simplifying the data, converting the data to gradient magnitude and direction data which is thresholded to simplify the data. Horizontal edges within the data are softened to reduce their masking of adjacent non-horizontal features. One or more target boxes are stepped across the image data and the number of directions of gradient direction data within the box is used to determine the presence of a target. Atmospheric attenuation is compensated. The thresholding used in one embodiment compares the gradient magnitude data to a localized threshold calculated from the local variance of the image gradient magnitude data. Imagery subsets are containing the candidate targets may then be used to detect and identify features and apply a classifier function to screen candidate detections and determine a likely target.

Abstract: A method and system are disclosed for estimating a position of moving objects in a set of image data. In accordance with exemplary embodiments of the present invention, a position of an object is identified in a first frame of image data acquired at a first time. The object is determined to be undetected in a second frame of image data acquired at a second time. Movement of the object is estimated to determine its estimated position in the second frame of image data using at least one of velocity and acceleration of the object and time between frames of image data. The estimated position is used to determine a position of the object in a third frame of image data acquired at a third time.

Abstract: A method of processing image data is described. The method comprises receiving first image data corresponding to a first image and second image data corresponding to a second image, wherein pixels of the first image data and pixels of the second image data are registered to each other. The method also comprises shifting at least a portion of the first image data by a first fractional pixel displacement and at least a portion of the second image data by a second fractional pixel displacement to generate first shifted data and second shifted data, respectively. The method also comprises interpolating the first shifted data and the second shifted data to generate first interpolated data and second interpolated data, respectively. The method further comprises differencing the first interpolated data and the second interpolated data to generate residue data. An image processing system comprising a memory and a processing unit configured to carry out the above-noted steps is also described.

Abstract: A method and apparatus for tracking an object in an image. An image of a scene comprising an object is received. The image is processed with a first tracker to produce a first estimate of a position of the object in the image and a first confidence value. The image is processed with a second tracker to produce a second estimate of the position of the object in the image and a second confidence value. The second tracker processes the image concurrently with the first tracker. An estimate of the position of the object in the image is selected using the first and second confidence values. The object is then tracked using the selected estimate.

Abstract: A method of processing image data is described. The method comprises receiving first image data corresponding to a first image and second image data corresponding to a second image, wherein pixels of the first image data and pixels of the second image data are registered to each other. The method also comprises shifting at least a portion of the first image data by a first fractional pixel displacement and at least a portion of the second image data by a second fractional pixel displacement to generate first shifted data and second shifted data, respectively. The method also comprises interpolating the first shifted data and the second shifted data to generate first interpolated data and second interpolated data, respectively. The method further comprises differencing the first interpolated data and the second interpolated data to generate residue data. An image processing system comprising a memory and a processing unit configured to carry out the above-noted steps is also described.

Abstract: A method and system detects candidate targets or objects from a viewed scene by simplifying the data, converting the data to gradient magnitude and direction data which is thresholded to simplify the data. Horizontal edges within the data are softened to reduce their masking of adjacent non-horizontal features. One or more target boxes are stepped across the image data and the number of directions of gradient direction data within the box is used to determine the presence of a target. Atmospheric attenuation is compensated. The thresholding used in one embodiment compares the gradient magnitude data to a localized threshold calculated from the local variance of the image gradient magnitude data. Imagery subsets are containing the candidate targets may then be used to detect and identify features and apply a classifier function to screen candidate detections and determine a likely target.

Abstract: A method and system are disclosed for estimating a position of moving objects in a set of image data. In accordance with exemplary embodiments of the present invention, a position of an object is identified in a first frame of image data acquired at a first time. The object is determined to be undetected in a second frame of image data acquired at a second time. Movement of the object is estimated to determine its estimated position in the second frame of image data using at least one of velocity and acceleration of the object and time between frames of image data. The estimated position is used to determine a position of the object in a third frame of image data acquired at a third time.