Component fusion for face detection
First Claim
1. A method for detecting an object in an object detection device, the method comprising:
- receiving a plurality of training images, at least one of the training images including a plurality of components of an object;
forming a statistical model from a mean and a covariance matrix of the components of each training image;
detecting a plurality of components in an input image using a component detector that is trained with the training images;
determining a covariance matrix for each of the detected components; and
matching a probabilistic observation comprising the covariance matrixes of the detected components with a probabilistic model comprising the mean and covariance matrixes of the statistical model to detect the object.
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Accused Products
Abstract
A system and method for object detection are provided where the system includes a component detection unit for detecting components in an image, a component fusion unit in signal communication with the component detection unit for fusing the components into an object, and a CPU in signal communication with the detection and fusion units for comparing the fused components with a statistical model; and the method includes receiving observation data for a plurality of training images, forming at least one statistical model from the plurality of training images, receiving an input image having a plurality of pixels, detecting a plurality of components in the input image, determining a fusion of the detected components, comparing the fusion with the statistical model, and detecting an object in accordance with the comparison.
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Citations
27 Claims
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1. A method for detecting an object in an object detection device, the method comprising:
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receiving a plurality of training images, at least one of the training images including a plurality of components of an object; forming a statistical model from a mean and a covariance matrix of the components of each training image; detecting a plurality of components in an input image using a component detector that is trained with the training images; determining a covariance matrix for each of the detected components; and matching a probabilistic observation comprising the covariance matrixes of the detected components with a probabilistic model comprising the mean and covariance matrixes of the statistical model to detect the object. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 27)
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9. A system for object detection, the system comprising:
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observation means for receiving a plurality of training images, at least one of the training images including a plurality of components of an object; modeling means for forming a statistical model from a mean and a covariance matrix of the components of each training image; a component detection unit for detecting components in an input image, wherein the component detection unit is trained using the training images; a component fusion unit in signal communication with the component detection unit for fusing the components; and a CPU in signal communication with said detection and fusion units for detecting the object by matching a probabilistic observation comprising the covariance matrixes of the detected components with a probabilistic model comprising the mean and covariance matrix of the statistical model. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for object detection, the method steps comprising:
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forming a statistical model from a mean M and a covariance matrix C of components of known example images; scanning locations in an input image using the statistical model to determine component confidence maps; identifying a local maximum from the confidence maps, wherein the local maximum has a location U; estimating a covariance matrix Q in an area around the local maximum; and matching a probabilistic observation comprising the location U and covariance matrix Q with a probabilistic model comprising the mean M and the covariance matrix C to determine whether the input image includes the object. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26)
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Specification