Foreground and Background Image Segmentation
First Claim
1. A computer-implemented method of segmenting a foreground portion from a background portion of an image having a plurality of image elements, each image element having an associated value, the method comprising:
- selecting a seed region in the foreground portion of the image;
calculating, using a processor, a geodesic distance from each image element to the seed region using the associated values;
determining a subset of the image elements having a geodesic distance less than a predefined threshold; and
labeling the subset of the image elements as foreground image elements.
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Accused Products
Abstract
Foreground and background image segmentation is described. In an example, a seed region is selected in a foreground portion of an image, and a geodesic distance is calculated from each image element to the seed region. A subset of the image elements having a geodesic distance less than a threshold is determined, and this subset of image elements are labeled as foreground. In another example, an image element from an image showing at least a user, a foreground object in proximity to the user, and a background is applied to trained decision trees to obtain probabilities of the image element representing one of these items, and a corresponding classification assigned to the image element. This is repeated for each image element. Image elements classified as belonging to the user are labeled as foreground, and image elements classified as foreground objects or background are labeled as background.
96 Citations
20 Claims
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1. A computer-implemented method of segmenting a foreground portion from a background portion of an image having a plurality of image elements, each image element having an associated value, the method comprising:
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selecting a seed region in the foreground portion of the image; calculating, using a processor, a geodesic distance from each image element to the seed region using the associated values; determining a subset of the image elements having a geodesic distance less than a predefined threshold; and labeling the subset of the image elements as foreground image elements. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer-implemented method of segmenting a foreground portion from a background portion of an image, comprising:
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receiving the image at a processor, wherein the image comprises a plurality of image elements, and the image represents at least one user, at least one foreground object in proximity to the at least one user, and a background; accessing at least one trained decision tree stored on a memory; selecting an image element from the image; applying the image element to the or each trained decision tree to obtain one or more probabilities of the image element representing part of a user, a foreground object or background; assigning a classification of user, foreground object or background to the image element in dependence on the one or more probabilities; repeating the steps of selecting, applying and assigning for each image element in the image; and labeling the image elements having the classification of user as the foreground portion, and the image elements having the classification of foreground object and background as the background portion. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A device for automatically segmenting a foreground portion from a background portion of an image, wherein the image comprises a plurality of image elements, each having an associated value, and the image represents a user, at least one foreground object in proximity to the user, and a background, the device comprising:
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an input interface arranged to receive the image from a capture device; at least one processor; and a memory arranged to store a decision forest comprising a plurality of distinct trained decision trees, and arranged to store executable instructions configured to cause the processor to; select a seed region in the foreground portion of the image;
calculate a geodesic distance from each image element to the seed region using the associated values;
determine a subset of the image elements having a geodesic distance within a predefined threshold distance of the seed region;
select an image element from the subset;
apply the subset image element to each of the trained decision trees to obtain a plurality of probabilities of the subset image element representing the user, the foreground object or the background;
aggregate the probabilities from each of the trained decision trees and assign a classification of user, foreground object or background to the subset image element in dependence thereon;
repeat the steps of selecting, applying and aggregating for each image element in the subset; and
output the subset image elements having the classification of user as the foreground portion. - View Dependent Claims (20)
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Specification