Method and system for estimating jointed-figure configurations
First Claim
1. A method for estimating the configuration of a figure in an image, comprising the steps of:
- generating a plurality of poses for a figure, each having an associated configuration that is defined by a plurality of configuration parameters;
for each pose, generating a representation image that is defined by a plurality of representation parameters;
computing an eigen-points model for figure configurations, based upon said configuration parameters and said representation parameters associated with said plurality of poses;
generating a representation image for a new image of a figure whose configuration is unknown;
applying the representation parameters for said new image to said model; and
generating a set of configuration parameters for the new image from said model.
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Accused Products
Abstract
To estimate the configuration of a figure in a captured image, a silhouette image of the figure is scanned to create a signed distance image. This image identifies the distance of each pixel in the image to the closest edge of the silhouette, and indicates whether the pixel is inside or outside of the silhouette. Multiple distance images of this type are employed to generate an eigen-points model, which provides an affine mapping from the signed distance images to the limb parameters of an authored skeleton. When a new input image is received, it is first processed to create the signed-distance image, and this image is applied to the eigen-points model to estimate limb parameters, such as the locations of various joints in the figure. From this information, each foreground pixel in the captured image can be assigned to one of the limbs.
440 Citations
40 Claims
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1. A method for estimating the configuration of a figure in an image, comprising the steps of:
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generating a plurality of poses for a figure, each having an associated configuration that is defined by a plurality of configuration parameters; for each pose, generating a representation image that is defined by a plurality of representation parameters; computing an eigen-points model for figure configurations, based upon said configuration parameters and said representation parameters associated with said plurality of poses; generating a representation image for a new image of a figure whose configuration is unknown; applying the representation parameters for said new image to said model; and generating a set of configuration parameters for the new image from said model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for determining the configuration of a limb on a figure, where the position of the limb can result from the combined movements of a plurality of jointed limbs, comprising the steps of:
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computing a first learned model which is based upon a first reference point that is determined with respect to a first limb of a figure, and using said first model to estimate the position of a second reference point on a second limb that is connected to said first limb; computing a second model which is based upon the second reference point, and using said second model to estimate the position of a third reference point relative to said second reference point; and combining the estimates for the second and third reference points to estimate the position of said third reference point relative to said first reference point. - View Dependent Claims (12, 13, 14, 15)
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16. In a system which estimates parameters of an image by means of a model which is based upon a data matrix comprising an input data matrix of image values and an output data matrix containing said parameters, a method comprising the steps of:
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(i) transforming the output data matrix by compressing variations in all rows of the output data matrix into a lesser number of rows, and applying the same transformation to the rows of the input data matrix; (ii) determining the relationship between (a) the coupling from each input dimension to the output variations in said lesser number of rows, and (b) the total variations in that input dimension; (iii) selecting one of said input dimensions on the basis of said determination; (iv) removing the selected input dimension and output variations which are coupled to the selected input dimension from the data matrix to produce a reduced data matrix; (v) iteratively repeating steps i-iv with respect to the reduced data matrix, to select additional input dimensions and coupled output variations; (vi) computing a model to estimate said parameters on the basis of said selected input dimensions and coupled output dimensions; and (vii) processing image values for an image whose parameters are unknown in accordance with said model to estimate the parameters for said image. - View Dependent Claims (17, 18, 19, 20)
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21. A method for estimating parameters which pertain to a visible image, comprising the steps of:
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developing an explicit hidden model which describes said parameters and variations of said parameters; using said hidden model to generate visual representations of images; training an eigen-points model on the basis of both said hidden model and said visual representations; generating a visual representation of an image whose parameters are unknown; and applying said visual representation to said eigen-points model to estimate said parameters for said image. - View Dependent Claims (22, 23)
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24. A method for estimating the configuration of a figure in an image, comprising the steps of:
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generating a plurality of poses for a figure, each having an associated configuration that is defined by a plurality of configuration parameters; for each pose, generating a signed distance image having values which identify the distance of pixels in the image from a border of the figure; computing a learned model for figure configurations, based upon said configuration parameters and said distance image values associated with said plurality of poses; generating a signed distance image for a new image of a figure whose configuration is unknown; applying the distance image values for said new image to said model; and generating a set of configuration parameters for the new image from said model. - View Dependent Claims (25, 26, 27, 28, 29, 30)
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31. A method for estimating the configuration of a figure in an image, comprising a learning phase and an estimation phase, wherein said learning phase includes the steps of:
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generating a plurality of poses for a figure, each having an associated configuration that is defined by a plurality of configuration parameters; for each pose, generating a representation image that is defined by a plurality of representation parameters; and computing a learned model for figure configurations, based upon said configuration parameters and said representation parameters associated with said plurality of poses; and wherein said estimation phase is carried out after the completion of said learning phase, and includes the steps of; generating a representation image for a new image of a figure whose configuration is unknown; applying the representation parameters for said new image to said model; and generating a set of configuration parameters for the new image from said model. - View Dependent Claims (32, 33, 34, 35)
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36. A method for determining the configuration of a limb on a figure, where the position of the limb can result from the combined movements of a plurality of jointed limbs, comprising the steps of:
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computing a first learned model which is based upon a first reference point that is determined with respect to a first limb of a figure, and using said first model to estimate a first region on a second limb that is connected to said first limb; determining a second reference point within said first region; computing a second model which is based upon the second reference point, and using said second model to estimate the position of a second region on the figure, relative to said second reference point; determining a third reference point within said second region; and combining the estimates for the first and second regions to estimate the position of said third reference point relative to said first reference point. - View Dependent Claims (37, 38, 39, 40)
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Specification