Image congealing via efficient feature selection
First Claim
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1. A method, comprising:
- integrating an unsupervised feature selection algorithm with a least-square based congealing algorithm;
selecting a subset of features from an initial feature representation with the unsupervised feature selection algorithm; and
executing the least-square based congealing algorithm to estimate warping parameters for a plurality of images in an ensemble using the subset of features.
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Abstract
A novel technique for unsupervised feature selection is disclosed. The disclosed methods include automatically selecting a subset of a feature of an image. Additionally, the selection of the subset of features may be incorporated with a congealing algorithm, such as a least-square-based congealing algorithm. By selecting a subset of the feature representation of an image, redundant and/or irrelevant features may be reduced or removed, and the efficiency and accuracy of least-square-based congealing may be improved.
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Citations
20 Claims
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1. A method, comprising:
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integrating an unsupervised feature selection algorithm with a least-square based congealing algorithm; selecting a subset of features from an initial feature representation with the unsupervised feature selection algorithm; and executing the least-square based congealing algorithm to estimate warping parameters for a plurality of images in an ensemble using the subset of features. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method, comprising:
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incorporating an unsupervised feature selection algorithm into an image congealing algorithm; executing the unsupervised feature selection algorithm, comprising; constructing a graph having features of an image as vertices; determining a connectivity between the vertices; partitioning the graph into two or more subsets of features; and selecting representative features from each subset of features; and executing the image congealing algorithm to estimate warping parameters for the image using the representative features. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17)
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18. A method, comprising:
minimizing a cost function of an image congealing process, comprising; selecting a subgroup of features for each of a plurality of original feature representations with an unsupervised feature selection algorithm, wherein each of the plurality of original feature representations corresponds to a respective image of a plurality of images in an ensemble; and estimating warping parameters for each of the plurality of images using the subgroups of features in a least-square-based congealing algorithm. - View Dependent Claims (19, 20)
Specification