Hughes Aircraft Company

Other

Jack M Sacks

Thomas K Lo

Nam D Banh

Wayne P Simoni

Timothy T Bohn

Franklin J Meyers

Guy B Coleman

Charles A McNary

Lynne M Karlin

Harrison Nguyen

Leonard A Lee

Kazuo R Hosokawa

Kelly D Holthaus

Andy L Quan

A process for reducing background clutter of a scene, and isolating an object of interest. The process includes calculating a minimum output from a plurality of appropriately configured anti-median filters representing a minimum difference processor (MDP) filter. In a first embodiment, the MDP filter includes a (5.times.5) matrix of elements wherein each element has an intensity value representation of the scene. A first anti-median filter array is configured as a horizontal array of 5 pixel elements in which the center element of the matrix is the center element of the horizontal array. A median value of the 5 elements of the horizontal array is determined, and is subtracted from the value of the center element to determine an anti-median value of the horizontal array. The same process is performed for a vertical 5 element anti-median array, and two diagonal 5 element anti-median arrays, all including the center element of the matrix as the center common element of the arrays. The lowest anti-median value is then outputted as the output of the MDP filter. In a second embodiment, the values of the remaining elements of the matrix which were not included in any of the horizontal, vertical or diagonal arrays described above are subtracted from the center element of the matrix. The lowest value from the group of anti-median values together with the remaining subtracted values is outputted from the MDP filter. This enables the background clutter of the scene to be substantially suppressed or eliminated.

Minimum difference processor

A method and apparatus for detecting an object of interest against a cluttered background scene. In a first preferred embodiment the sensor tracking the scene is movable on a platform such that each frame of the video representation of the scene is aligned, i.e., appears at the same place in sensor coordinates. A current video frame of the scene is stored in a first frame storage device (14) and a previous video frame of the scene is stored in a second frame storage device (20). The frames are then subtracted by means of an invertor (24)and a frame adder (28) to remove most of the background clutter. The subtracted image is put through a first leakage reducing filter, preferably a minimum difference processor filter (32). The current video frame in the first frame storage device (14) is put through a second leakage-reducing filter, preferably minimum difference processor filter (36). The outputs of the two processors are applied to a two quadrant multiplier (42) to minimize the remaining background clutter leakage and to isolate the moving object of interest. In a second preferred embodiment the sensor does not track the scene and therefore the frames of video from the two storage devices (140, 200) must be aligned before the subtraction process can occur. The alignment function is performed by a background registration correlator (150) and a frame shifter (130). Once the two frames of video data are aligned they are then subtracted and preferably applied to the minimum difference processor filters (320, 360) as with the first embodiment. The outputs of the two minimum difference processors (320, 360) are again applied to a two quadrant multiplier (420) to eliminate the remaining background clutter.

Moving target detection method using two-frame subtraction and a two

Three image frames containing an object of interest and background clutter are taken at successive time intervals and stored in memory. The background of images A, B and C are registered preferably using an area correlator 12. A median filter 16 is used to select a median value from the registered image frames. Then, subtractor 18 serves to subtract the median pixel values from one of the image frames. This difference output is then thresholded to provide a binary signal whose pixel values exceeding the threshold levels are generally associated with the position of the moving object.

Segmentation method for use against moving objects

Target tracking capability is provided by a synergistic tracker system (5) which includes both a correlation tracker (30) and an object tracker (40) for processing sensor data input (10) and for generating tracking error signals (80). The operation of the synergistic tracker system is controlled by a central processing unit (70). The system operates by first correlating a reference region image with a portion of a current digitized image provided by an analog to digital converter (20). Secondly, the object tracker provides a precisely defined trackpoint for an object within the current image. The correlation tracker stabilizes and limits the portion of the digitized image that the object tracker must operate upon. Stabilizing and limiting this portion of the digitized image reduces the object tracker's sensitivity to background clutter and sensitivity to a loss of lock induced by sensor motion. The object tracker provides a non-recursive update for the correlation's reference region image. The correlation tracker and the object tracker are used simultaneously and cooperatively so that the strengths of one tracker are used to overcome the weaknesses of the other. A method of tracking a target immersed in background clutter is also disclosed.

Apparatus and method for tracking a target

A method and apparatus for correlating two signals representing a live image and a reference image are disclosed. The reference signal is processed to provide a polarity bit and mask bit for each pixel position in the reference image. The processed video signal is stored in a memory 20 and used in a convolver and summer section 18 in which the polarity bits between the reference and live images are gated by gate 17 and are used in generating a correlation output from summer 23. However, the polarity bits do not affect the correlation output if the logical output from gate 19 is of a given state which occurs if the associated mask bits indicate that the gradient value at the particular pixel position does not exceed certain minimum threshold values.

Maskable bilevel correlators

A conventional tracker 14 is used to continually aim a sensor 12 at a flying object so that it is located in substantially the same spatial location in a plurality of image frames taken at different times. Thus, the object remains in about the same spatial position in each image frame while the background clutter generally changes. By processing two or more of the image frames it is possible to calculate a more precise aimpoint for the object than is generally provided by the track point.

Segmentation method for terminal aimpoint determination on moving

A method and apparatus are disclosed for detecting moving objects silhouetted against background clutter. A correlation subsystem 12 is used to register the background of a current image frame (framestore 1) with an image frame taken two time periods earlier (framestore 3). A first difference image is generated by subtracting the registered images, with the first difference image being filtered by low pass filter 34 and thresholded by a three level segmenter 36. A second difference image is likewise generated between the current image frame and another image frame taken at a different subsequent time period. This second difference image is likewise filtered and thresholded. The two difference images are logically ANDed together by gate 39 whose output is connected to a formatter, sorter and feature extractor 40. Track files are generated in CPU 16 for comparing subsequently extracted image features with previously stored extracted features.

Signal processing for autonomous acquisition of objects in cluttered

A system (20) for identifying and tracking targets in an image scene having a highly cluttered background. An imaging sensor and processing subsystem (21) provides a video image of the image scene. A size identification subsystem (23) removes background clutter from the image by filtering the image to pass objects whose sizes are within a predetermined size range. A feature analysis subsystem (24) analyzes the features of those objects which pass through the size identification subsystem and determines if a target is present in the image scene. A gated tracking subsystem (25) and scene correlation and tracking subsystem (26) track the target objects and image scene, respectively, until a target is identified. Thereafter, the tracking subsystems lock onto the target identified by the system (20). Several methods relating to target acquisition are also disclosed.

Target acquisition system and method

Apparatus for processing applied signals indicative of an image scene in order to provide output signals representative of objects in the scene whose sizes are within a predetermined size range. Combinations of median and anti-median filters are employed in serial and parallel configurations to produce the desired size filtering. The basic system comprises first filter means for providing first output signals indicative of objects whose sizes are within a first preselected size range. Second filter means are employed which provide second output signals indicative of objects whose sizes are within a second preselected size range. At least one of the two preselected size ranges contain object sizes within the predetermined size range. Circuit means are provided for coupling the output signals from the two filters in a manner which generates output signals indicative of objects whose sizes are within the predetermined range. A method of processing applied signals to generate output signals containing objects whose sizes are within a predetermined size range is also disclosed.

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Thousand Oaks, CA, United States of America

Jack M Sacks