Nonparametric imaging tracker
First Claim
1. An imaging tracker, comprising:
- means for declaring a reference subimage including a two-dimensional region of intensities within an image;
means for determining a reference distribution of intensities in said reference subimage;
means for acquiring a plurality of candidate subimages;
means for determining a candidate distribution of intensities for each of said candidate subimages;
means for comparing each said candidate distribution to said reference distribution;
means for computing a statistic for each candidate subimage, said statistic being a single number based on the comparison of said candidate distribution and said reference distribution; and
means for selecting one of said candidate subimages based on its respective statistic.
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Abstract
A nonparametric imaging tracker is disclosed that first declares (10) a reference subimage comprising a plurality of image pixels, each pixel having a numerical light intensity level. The tracker then finds (12) a reference distribution of intensity levels in the reference subimage. A plurality of candidate subimages each having a plurality of pixels are then acquired (20). The intensity level distribution of each candidate subimage is compared to the reference distribution, and a statistic for each candidate subimage is computed based on this comparison (26, 28). One of the candidate subimages is then selected based on the value of its statistic (30, 36, 40). A preferred comparison and statistic calculation sequence includes the Kolmogorov-Smirnov test.
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Citations
32 Claims
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1. An imaging tracker, comprising:
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means for declaring a reference subimage including a two-dimensional region of intensities within an image; means for determining a reference distribution of intensities in said reference subimage; means for acquiring a plurality of candidate subimages; means for determining a candidate distribution of intensities for each of said candidate subimages; means for comparing each said candidate distribution to said reference distribution; means for computing a statistic for each candidate subimage, said statistic being a single number based on the comparison of said candidate distribution and said reference distribution; and means for selecting one of said candidate subimages based on its respective statistic.
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2. A nonparametric imaging tracker for tracking a designated object within a scene, comprising:
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an attainer for attaining a reference subimage of said object from an initial image of said scene; a finder for determining a reference intensity distribution for said reference subimage; an acquirer for acquiring a plurality of candidate subimages within an image received subsequent to receiving said initial image; said finder operable to determine an intensity distribution for each candidate subimage; a calculator for calculating a statistic for each candidate subimage, said statistic being a single number based on a comparison of a respective candidate intensity distribution with said reference intensity distribution; and a selector for selecting one of said candidate subimages as a subimage of said object based on the value of each said candidate statistic. - View Dependent Claims (3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A nonparametric imaging tracker for tracking an object within a scene, comprising:
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an acquirer for acquiring an initial reference subimage of said object; a quantifier for determining a gray level for each of a plurality of pixels in said reference subimage; a finder for finding a reference gray level histogram of said reference subimage; an integrator for integrating said reference gray level histogram to yield a reference gray level distribution of said reference subimage; said acquirer operable to acquire a plurality of candidate subimages, each said candidate subimage having a plurality of pixels; said quantifier operable to determine a gray level for each pixel in each candidate subimage, said finder operable to find a candidate gray level histogram for each candidate subimage, said integrator operable to integrate a candidate gray level distribution for each candidate subimage; a calculator for calculating a Kolmogorov-Smirnov statistic for each candidate subimage; a selector for selecting the lowest of said Kolmogorov-Smirnov statistics; and an object location updater for updating the location of said object in said scene based on the location of said selected candidate subimage. - View Dependent Claims (16, 17, 18, 19)
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20. A method for tracking an object in a scene comprising the steps of:
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declaring a reference subimage of said object comprising a plurality of image pixels, each pixel having a numerical intensity level; finding a reference distribution of intensity levels in the reference subimage; acquiring a plurality of candidate subimages each having a plurality of pixels; finding a candidate subimage distribution of intensity levels for each of the candidate subimages; comparing each candidate subimage distribution to the reference distribution; computing a statistic for each candidate subimage, said statistic being a single number based on said step of comparing; and selecting one of the candidate subimages based on its respective statistic. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
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32. A method for tracking an imaged object within a scene, comprising the steps of:
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obtaining an initial reference subimage of the object comprising a plurality of pixels, each pixel having a numerical gray level; finding a reference gray level histogram for the reference subimage; computing a reference gray level distribution from the reference gray level histogram; storing the reference gray level distribution; acquiring a plurality of candidate subimages within a new image of the scene; finding a candidate gray level histogram for each of the candidate subimages; computing candidate gray level distribution from each candidate gray level histogram; computing a Kolmogorov-Smirnov statistic Δ
2 for each candidate subimage according to the following formula;
##EQU6## wherein R is a linear array of reference distribution gray level members, S is a linear array of candidate distribution gray level members, and I is a gray level member index;selecting the smallest of the statistics; and declaring a new object location in the scene based on the locus of the corresponding candidate subimage.
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Specification