Canonical correlation analysis of image/control-point location coupling for the automatic location of control points
First Claim
1. A method for determining continuous-valued hidden data from observable data, comprising the steps of:
- A) conducting a training stage which includes the steps of;
labelling a plurality of representative sets of unaligned observed data to identify correct alignment of the observed data and continuous-valued hidden data associated with each set of observed data;
analyzing the observed data to generate a first model which represents the aligned observed data;
generating a second model on the aligned and labeled data sets which explicitly represents the coupling between aligned observable data and the hidden data;
B) for each set of unlabeled data, conducting a labelling stage which includes the steps of;
analyzing the unlabeled set of unaligned observed data by means of the first model to determine alignment of the observable data associated therewith;
applying the second model to said unlabeled set of aligned observed data; and
determining hidden data for the unlabeled set of aligned data from said application of the second model.
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Abstract
The identification of hidden data, such as feature-based control points in an image, from a set of observable data, such as the image, is achieved through a two-stage approach. The first stage involves a learning process, in which a number of sample data sets, e.g. images, are analyzed to identify the correspondence between observable data, such as visual aspects of the image, and the desired hidden data, such as the control points. Two models are created. A feature appearance-only model is created from aligned examples of the feature in the observed data. In addition, each labeled data set is processed to generate a coupled model of the aligned observed data and the associated hidden data. In the second stage of the process, the modeled feature is located in an unmarked, unaligned data set, using the feature appearance-only model. This location is used as an alignment point and the coupled model is then applied to the aligned data, giving an estimate of the hidden data values for that data set.
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Citations
35 Claims
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1. A method for determining continuous-valued hidden data from observable data, comprising the steps of:
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A) conducting a training stage which includes the steps of;
labelling a plurality of representative sets of unaligned observed data to identify correct alignment of the observed data and continuous-valued hidden data associated with each set of observed data;
analyzing the observed data to generate a first model which represents the aligned observed data;
generating a second model on the aligned and labeled data sets which explicitly represents the coupling between aligned observable data and the hidden data;
B) for each set of unlabeled data, conducting a labelling stage which includes the steps of;
analyzing the unlabeled set of unaligned observed data by means of the first model to determine alignment of the observable data associated therewith;
applying the second model to said unlabeled set of aligned observed data; and
determining hidden data for the unlabeled set of aligned data from said application of the second model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35)
i) selecting possible locations for the alignment of the data;
ii) for each possible location, determining a lower bound for the distance between the unlabeled data set aligned at that location and an expected appearance of aligned data, in accordance with an average appearance defined by the first model;
iii) removing the possible locations whose lower bound exceeds a threshold value;
iv) for each possible location, determining the coordinate value for a dimension of the first model;
v) for each possible location, determining a new lower bound by combining previously determined coordinate values with the distance between the data set aligned at that location and the appearance of the data set under said alignment in accordance with the previously determined coordinate values; and
vi) repeating steps iii), iv) and v) for all of the dimensions of the model.
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24. The method of claim 23 wherein said lower bounds are determined in accordance with expected variances along each of the dimensions of the manifold model.
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25. The method of claim 24 wherein said expected variances are progressively smaller on each successive repetition of said steps.
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26. The method of claim 22 wherein the step of applying the second model to the unlabeled set of aligned observed data includes
projecting, with the use of an orthonormal transform, the aligned observed unlabeled data onto a subspace of the second model having fewer dimensions than said second model; -
performing a general matrix multiplication within said subspace; and
projecting, with the use of an orthonormal transform, into a second space of the model to determine hidden data for the unlabeled data set.
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27. The method of claim 26 wherein said general matrix multiplication is determined, in part, according to a gradual roll-off in manifold dimensions according to the coherence between the hidden and aligned observed data that is used to generate said second model.
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28. The method of claim 1 further including the step defining the alignment of the observed data in the representative sets of data from an analysis of the hidden data with which the data sets are labeled.
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29. The method of claim 28 wherein an analysis of the observed data is also employed in said alignment process.
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30. The method of claim 28 wherein said defining step comprises dividing the hidden data into separate groups, and assigning a different definition of aligned observed data in each representative data set to the respective groups.
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31. The method of claim 30 wherein the division of the hidden data into separate groups is determined in accordance with analysis of the hidden data.
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32. The method of claim 30 wherein the definition of aligned observed data is determined in accordance with analysis of the hidden data.
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33. The method of claim 32 wherein the definition of aligned observed data is also determined in accordance with analysis of the observed data.
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34. The method of claim 31 wherein the observed data is also used to divide the hidden data into said groups.
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35. The method of claim 32 wherein the division of hidden data into groups is carried out by measuring the coherence of the hidden data.
Specification