Image registration with uncertainty analysis
First Claim
Patent Images
1. An image registration method comprising:
- acquiring a first image and a second image of a scene using a processor to perform the steps of;
detecting edges in the first image and the second image;
calculating a percentage of pixels corresponding to a detected edge in a subset of the second image that match a detected edge in the first image shifted by a translation;
defining a best registration point based on a maximum percentage of edges matched;
applying within a pre-defined search region an alpha-level statistical test of a null hypothesis that average match probabilities are equal between the best registration point and each remaining point in the pre-defined search region;
using the alpha-level statistical test to identify all registration points within the pre-defined search region, other than the best registration point, that have a p-value exceeding a specified alpha level; and
determining a 100(1-alpha) % statistical confidence set consisting of the best registration point and all other registration points having the p-value exceeding the specified alpha level, in the alpha-level statistical test of the null hypothesis.
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Abstract
In an image registration method, edges are detected in a first image and a second image. A percentage of edge pixels in a subset of the second image that are also edges in the first image shifted by a translation is calculated. A best registration point is calculated based on a maximum percentage of edges matched. In a predefined search region, all registration points other than the best registration point are identified that are not significantly worse than the best registration point according to a predetermined statistical criterion.
35 Citations
16 Claims
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1. An image registration method comprising:
acquiring a first image and a second image of a scene using a processor to perform the steps of; detecting edges in the first image and the second image; calculating a percentage of pixels corresponding to a detected edge in a subset of the second image that match a detected edge in the first image shifted by a translation; defining a best registration point based on a maximum percentage of edges matched; applying within a pre-defined search region an alpha-level statistical test of a null hypothesis that average match probabilities are equal between the best registration point and each remaining point in the pre-defined search region; using the alpha-level statistical test to identify all registration points within the pre-defined search region, other than the best registration point, that have a p-value exceeding a specified alpha level; and determining a 100(1-alpha) % statistical confidence set consisting of the best registration point and all other registration points having the p-value exceeding the specified alpha level, in the alpha-level statistical test of the null hypothesis. - View Dependent Claims (5, 6, 7, 8)
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2. An image registration method comprising:
acquiring a first image and a second image of a scene using a processor to perform the steps of; detecting edges in the first image and the second image; conducting registration over a subset of pixels detected as edges in the second image for a total of N edge pixels; translating according to a candidate translation (h,k) with a row shift h and a column shift k between the first and second images within a user-selected range of shifts; calculating a percentage p(h,k) of the N edge pixels that also correspond to edges in the first image shifted by the candidate translation (h,k); analyzing match percentages p(h,k) over a set of the candidate translations (h,k) to generate a set of analyzed match percentages p(h,k); defining a maximum match percentage as a maximum of the set of analyzed match percentages p(h,k); defining a best registration point based on a maximum percentage of edges matched; determining whether the maximum match percentage enables registration at a predefined confidence level; and if the maximum match percentage enables registration, statistically determining which candidate translations (h,k) have match percentages p(h,k) that are not significantly lower than the maximum match percentage. - View Dependent Claims (3, 4)
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9. A system adapted for image registration comprising:
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an edge detector adapted to transform a first grayscale image and a second grayscale image from grayscale to binary format to form a first binary format image and a second binary format image; a transformation engine adapted to register translation between detected edges in the first binary format image and the second binary format image; and a controller adapted to calculate a percentage of pixels corresponding to a detected edge in a subset of the second binary format image that match a detected edge in the first binary format image shifted by a translation, define a best registration point according to a maximum percentage of edges matched, apply within a pre-defined search region an alpha-level statistical test of a null hypothesis that average match probabilities are equal between the best registration point and each remaining point in the pre-defined search region, use the alpha-level statistical test to identify all registration points within the pre-defined search region, other than the best registration point, that have a p-value exceeding a specified alpha level, and determine a 100(1−
alpha) % statistical confidence set consisting of the best registration point and all other registration points having the p-value exceeding the specified alpha level, in the alpha-level statistical test of the null hypothesis. - View Dependent Claims (13, 14, 15)
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10. A system adapted for image registration comprising:
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an edge detector adapted to transform a first grayscale image and a second grayscale image from grayscale to binary format to form a first binary format image and a second binary format image; a transformation engine adapted to register translation between detected edges in the first binary format image and the second binary format image, wherein the transformation engine is adapted to conduct registration over a subset of pixels detected as edges in the second image for a total of N edge pixels and translate according to a candidate translation (h,k) with a row shift h and a column shift k between the first and second images within a range of shifts; and a controller adapted to calculate a percentage of pixels corresponding to a detected edge in a subset of the second binary format image that match a detected edge in the first binary format image shifted by a translation, define a best registration point according to a maximum percentage of edges matched, apply within a search region an alpha-level statistical test of a null hypothesis that average match probabilities are equal between the best registration point and each remaining point in the search region, use the alpha-level statistical test to identify all registration points within the search region, other than the best registration point, that have a p-value exceeding a specified alpha level, and determine a 100(1-alpha) % statistical confidence set consisting of the best registration point and all other registration points having the p-value exceeding the specified alpha level, in the alpha-level statistical test of the null hypothesis, wherein the controller is adapted to calculate a percentage p(h,k) of the N edge pixels that correspond to detected edges in the first image shifted by the candidate translation (h,k), analyze match percentages p(h,k) over a set of candidate translations (h,k) to generate analyzed match percentages p(h,k), define a maximum match percentage as a maximum of the analyzed match percentages p(h,k), determine whether the maximum match percentage enables registration, and if the maximum match percentage enables registration, statistically determine which candidate translations (h,k) have match percentages p(h,k) that are not significantly lower than the maximum match percentage. - View Dependent Claims (11, 12)
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16. A system adapted for image registration comprising:
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an edge detector; a transformation engine coupled to the edge detector and adapted to receive a first image and a second image from the edge detector and to register translation over a subset of pixels detected as edges in the second image for a total of N edge pixels and translate according to a candidate translation (h,k) with a row shift h and a column shift k between the first image and the second image within a range of shifts; and a controller adapted to calculate a percentage p(h,k) of the N edge pixels that match edges in the first image shifted by the candidate translation (h,k), analyze match percentages p(h,k) over a set of candidate translations (h,k), determine whether a maximum match percentage is sufficient to enable registration to a confidence level, apply if the maximum match percentage is sufficient an alpha-level statistical test of a null hypothesis that average match probabilities are equal between the best registration point and each remaining point in the pre-defined search region, use the alpha-level statistical test to identify all registration points within the pre-defined search region, other than the best registration point, that have a p-value exceeding a specified alpha level, and determine a 100(1-alpha) % statistical confidence set consisting of the best registration point and all other registration points having the p-value exceeding the specified alpha level, in the alpha-level statistical test of the null hypothesis.
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Specification