Image Segmentation Using Reduced Foreground Training Data
First Claim
1. A computer-implemented method of segmenting an image into foreground and background portions, the method comprising:
- determining a foreground training region and a background training region of the image;
determining foreground and background properties based on said foreground and background training regions; and
computing foreground and background portions of the image by optimizing a first energy function,wherein the first energy function comprises a function of the foreground and background properties, andwherein the step of determining a foreground training region and a background training region of the image comprises;
receiving, at a computer, a user input defining a region of the image;
on the basis of the user input, segmenting the image into a first portion comprising image elements having a foreground label and a second portion of image elements having a background label, wherein the foreground label comprises a segmentation parameter which has a first value and the background label comprises a segmentation parameter which has a second value;
defining a second energy function comprising a combination of the first energy function and an additional term, the additional term comprising a combination of a scalar value and the segmentation parameter for an image element, summed over a plurality of image elements;
optimizing the second energy function using a plurality of different values of the scalar value to produce a plurality of optimization results, each optimization result defining a candidate foreground training region and a candidate background training region; and
selecting one of the plurality of optimization results to provide the foreground and background training regions.
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Accused Products
Abstract
Methods of image segmentation using reduced foreground training data are described. In an embodiment, the foreground and background training data for use in segmentation of an image is determined by optimization of a modified energy function. The modified energy function is the energy function used in image segmentation with an additional term comprising a scalar value. The optimization is performed for different values of the scalar to produce multiple initial segmentations and one of these segmentations is selected based on pre-defined criteria. The training data is then used in segmenting the image. In other embodiments further methods are described: one places an ellipse inside the user-defined bounding box to define the background training data and another uses a comparison of properties of neighboring image elements, where one is outside the user-defined bounding box, to reduce the foreground training data.
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Citations
20 Claims
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1. A computer-implemented method of segmenting an image into foreground and background portions, the method comprising:
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determining a foreground training region and a background training region of the image; determining foreground and background properties based on said foreground and background training regions; and computing foreground and background portions of the image by optimizing a first energy function, wherein the first energy function comprises a function of the foreground and background properties, and wherein the step of determining a foreground training region and a background training region of the image comprises; receiving, at a computer, a user input defining a region of the image; on the basis of the user input, segmenting the image into a first portion comprising image elements having a foreground label and a second portion of image elements having a background label, wherein the foreground label comprises a segmentation parameter which has a first value and the background label comprises a segmentation parameter which has a second value; defining a second energy function comprising a combination of the first energy function and an additional term, the additional term comprising a combination of a scalar value and the segmentation parameter for an image element, summed over a plurality of image elements; optimizing the second energy function using a plurality of different values of the scalar value to produce a plurality of optimization results, each optimization result defining a candidate foreground training region and a candidate background training region; and selecting one of the plurality of optimization results to provide the foreground and background training regions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer-implemented method of segmenting an image into foreground and background portions, the method comprising:
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determining a foreground training region and a background training region of the image; determining foreground and background properties based on said foreground and background training regions; and computing foreground and background portions of the image by optimizing a first energy function, wherein the first energy function comprises a function of the foreground and background properties, and wherein the step of determining a foreground training region and a background training region of the image comprises; receiving, at a computer, a user input defining a region of the image; assigning a background label to at least a subset of image elements outside the user-defined region; assigning a background label to an image element in the user-defined region which has a neighbor image element assigned a background label if a difference in properties associated with the image element and the neighbor image element is less than a defined threshold; defining the background training region as those image elements assigned a background label and defining the foreground training region as other image elements within the user-defined region. - View Dependent Claims (14, 15, 16)
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17. One or more tangible device-readable media with device-executable instructions that, when executed by a computing system, direct the computing system to perform steps comprising:
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displaying, on a display device, an image to a user; receiving a user input defining a region of the image; on the basis of the user input, determining a foreground training region and a background training region of the image; determining foreground and background properties based on said foreground and background training regions; computing foreground and background portions of the image by iteratively optimizing a first energy function, wherein the first energy function comprises a function of the foreground and background properties; and displaying, on a display device, at least one of the foreground and background portions of the image, wherein the step of determining a foreground training region and a background training region of the image comprises; on the basis of the user input, segmenting the image into a first portion comprising image elements having a first opacity value and a second portion of image elements having a second opacity value; defining a second energy function comprising the first energy function and an additional term, the additional term comprising a product of a scalar and an opacity value for an image element, summed over all image elements; iteratively optimizing the second energy function using increasing values of the scalar to produce an optimization result defining a candidate foreground training region and a candidate background training region which satisfy predefined criteria; using said optimization result to provide the foreground and background training regions. - View Dependent Claims (18, 19, 20)
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Specification