Image segmentation of foreground from background layers
First Claim
1. A computer readable storage media containing computer readable instructions that, when executed by a computing device, cause the computing device to perform:
- determining one or more motion parameters of a motion model by fitting the motion model to a distribution of temporal derivatives or spatial gradients of training pixels from a set of training images, individual training pixels from the set of training images having previously been labeled as foreground or background training pixels;
determining a motion likelihood for input pixels of an input image, the motion likelihood being determined using the one or more motion parameters of the motion model without a velocity of the input pixels;
determining a color likelihood for segmenting the input pixels of the input image; and
automatically generating segmentation indicators for the input pixels of the input image based on the motion likelihood and the color likelihood, the segmentation indicators indicating whether individual input pixels are foreground input pixels or background input pixels.
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Abstract
Segmentation of foreground from background layers in an image may be provided by a segmentation process which may be based on one or more factors including motion, color, contrast, and the like. Color, motion, and optionally contrast information may be probabilistically fused to infer foreground and/or background layers accurately and efficiently. A likelihood of motion vs. non-motion may be automatically learned from training data and then fused with a contrast-sensitive color model. Segmentation may then be solved efficiently by an optimization algorithm such as a graph cut. Motion events in image sequences may be detected without explicit velocity computation.
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Citations
20 Claims
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1. A computer readable storage media containing computer readable instructions that, when executed by a computing device, cause the computing device to perform:
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determining one or more motion parameters of a motion model by fitting the motion model to a distribution of temporal derivatives or spatial gradients of training pixels from a set of training images, individual training pixels from the set of training images having previously been labeled as foreground or background training pixels; determining a motion likelihood for input pixels of an input image, the motion likelihood being determined using the one or more motion parameters of the motion model without a velocity of the input pixels; determining a color likelihood for segmenting the input pixels of the input image; and automatically generating segmentation indicators for the input pixels of the input image based on the motion likelihood and the color likelihood, the segmentation indicators indicating whether individual input pixels are foreground input pixels or background input pixels. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method comprising:
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determining, on at least one computing device comprising at least one processing unit, a motion likelihood for input pixels of an input image, the motion likelihood being determined using a motion model without computing full velocities of the input pixels, the motion model having at least one parameter that is fitted to training images having training segmentation indicators distinguishing background pixels from foreground pixels in the training images; determining, on the at least one computing device, a color likelihood for segmenting the input pixels of the input image based on a color likelihood model; automatically generating, on the at least one computing device, a segmentation indicator associated with at least one of the input pixels, the segmentation indicator being based on the motion likelihood and the color likelihood; and storing, on a computer storage media, the segmentation indicator. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15)
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16. A system comprising:
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a segmentation module configured to; determine a motion likelihood for input pixels of an input image, the motion likelihood being determined using a motion model without explicitly calculating velocities of each of the input pixels, the motion model having at least one parameter that is fitted to training images having training segmentation indicators distinguishing background pixels from foreground pixels in the training images; determine a color likelihood for segmenting the input pixels of the input image based on a color likelihood model; and generate segmentation indicators associated with the input pixels, the segmentation indicators being generated based on a combination of the motion likelihood and the color likelihood; and at least one processing unit configured to execute the segmentation module. - View Dependent Claims (17, 18, 19, 20)
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Specification