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)
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Specification