Image analysis – Image enhancement or restoration – Edge or contour enhancement
Reexamination Certificate
2000-10-10
2002-09-03
Patel, Jayanti K. (Department: 2623)
Image analysis
Image enhancement or restoration
Edge or contour enhancement
C382S103000, C382S209000
Reexamination Certificate
active
06445832
ABSTRACT:
BACKGROUND
The present invention relates to image processing. More particularly, the present invention relates to image processing in target tracking systems.
Image target tracking is normally considered an autonomous process, which locates and thus tracks a particular predetermined or marked object in each successive frame of an image sequence. Image sequences for target tracking can be generated via electro optical sensors or through synthetic means such as computer generation which produce a synthesized image from, for example, data collected from non-optical sensors or, alternatively, may be produced from simulation data. The image sequence through which an object is tracked in a typical sequence usually amounts to a relatively stable video image when viewed at a constant repetition rate. Problems arise however when there is a large degree of movement of a tracked object within an image frame, particularly when the movement is discontinuous, e.g. there are large non-smooth movements from one frame to another. It becomes difficult for prior art target trackers to remain fixed on a selected target both as the scene moves forward in time through the image frames which comprise the sequence and the target moves about through the scene.
Modern target trackers fall into different categories depending on how they process information related to tracking a target or object. Feature based trackers, for example, process an image to first identify and segment out a candidate region for tracking in which a target is most likely to be present. Then, feature measures may be made of the candidate region. On the next frame of data the same process is applied to a region where the target can be found. Since the overall search region, e.g. the image frame, in the next image of the sequence is larger than the candidate or template region segmented from the original image frame, more than one segmented region may be extracted. If this is the case all segmented regions are treated the same and feature measurements are made of each. Next, it may be determined which set of features best matches the template, typically by using, for example, a distance measure in feature space. Accordingly, the set of features “closest” to the template in terms of the distance metric is nominated as the new target track location. It should be noted that while feature based trackers are effective at tracking targets which remain fairly stationary within the frame, the computational load increases for scenes which contain multiple targets and/or scenes where targets are characterized by high degrees of motion including discontinuous inter-frame motion.
Another category of target trackers includes correlation trackers. Correlation trackers uses the template image to run a cross correlation with, for example, a search window or region to find the best match or highest degree of correlation between the template and the contents of the search window or search region. Correlation trackers evolved from trackers as described above, but with added constraints imposed on accomplishing the goal tracking, e.g. limited computing power, limited memory, and the like. Some original trackers included severe constraints in that they were primarily built using analog circuits and performed target tracking processing using discrete analog circuits.
In U.S. Pat. No. 5,724,435 to Malzbender, a digital filter and method of tracking a structure extending in three spatial dimensions is described wherein a 3D model is used as a stored reference which is compared with an image object to attempt to obtain a match. All rotations of the reference model are compared to get a best match. U.S. Pat. No. 5,351,310 to Califano et al. describes a generalized shape autocorrelation for shape acquisition and recognition. Objects may be tracked using a model of a target of interest. Tracking quantities primarily are local shape descriptors. In U.S. Pat. No. 4,644,585 to Crimminset al., a method and apparatus are described for automatic shape recognition. In this Automated Target Recognition system internal shape of an object may be used to make a recognition match however no tracking is described. In U.S. Pat. No. 5,960,097 to Pfciffer et al., a system for background adaptive target detection and tracking with multiple observation and processing stages is described wherein target tracking operators point type tracking approaches of partial resolved targets. In U.S. Pat. No. 5,657,251 to Fiala, a system and process for performing optimal target tracking, is described addressing a track file technique used in tracking multiple targets from frame to frame. Un U.S. Pat. No. 5,748,775 to Tsuchikawa et al. a method and apparatus are described for moving object extraction based on background subtraction. The approach uses background removal to determine residue information that can be determined to be a moving object. In U.S. Pat. No. 5,062,056 to Lo et al., a method and apparatus are described for tracking a target immersed in background clutter. An object tracker and a correlation tracker are used synergistically to provide tracking error signals. In U.S. Pat. No. 5,947,413 to Mahalanobis a correlation filter for target reacqusition is described. Therein an iterative process includes DCCFs, DCCP, and MACH filters to update aim-point and re-designate targets. In U.S. Pat. No. 6,005,609 to Cheong, a method and apparatus is described for digital correlation object tracking using a shape extraction focalization.
SUMMARY
Accordingly, a method and apparatus are described for tracking an object image in an image sequence. The method and apparatus may provide precision tracking of the object image, which may be an actual target or which may be an object, such as a virtual target provided from, for example, a simulator.
Thus, in accordance with various exemplary embodiments of the present invention, a template window associated with the object image from a first image in the image sequence may be established and a first edge gradient direction associated with the template window extracted therefrom. A search window associated with the object image may be established from subsequent images in the image sequence. A second edge gradient direction associated with the search window may then be extracted. It should be noted that the search and template windows may be established using a process such as feature extraction or the like. In order to provide balanced tracking in an exemplary correlator, the first and second edge gradient directions may be weighted according to weights or weighting coefficients which allow contributions from edges of different strengths to be normalized, adjusted or otherwise balanced. Accordingly, weighting may further include calculating a set of weighting coefficients for the edge gradient direction associated with the search window and the template window respectively such that a balanced contribution is provided from edge gradient directions associated with the search window and the template window in the step of correlating. It should be noted that balancing may include equal weighting among edge gradient directions, or alternatively may include weighting to achieve, for example, emphasis and de-emphasis according to, for example, the relative strength or weakness of edges. The first and second edge gradient directions may further be weighted to track the object image over one or more images in the image sequences.
Further in accordance with various exemplary embodiments of the present invention, correlation may further include providing a coordinate offset between a coordinate position of the object image in the search window and a coordinate position of the object image in the template window. It should be noted that if the coordinate offset provided during correlation exceeds a predetermined value, the template window may be updated by establishing a new template window from the image sequence using, for example, the current image in the image sequence or a subsequent image from the sequence after the offset which exceeds the predetermin
Lee Harry C.
Nguyen Dat
Burns Doane , Swecker, Mathis LLP
Lockheed Martin Corporation
Patel Jayanti K.
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