Patent US6807304B2 successfully refreshed from Google Patents.
US6807304B2
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Feature recognition using loose gray scale template matching
Assignee: Xerox Corp
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Filing: 2000-12-26
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Granted: 2004-10-19
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Expires: 2020-12-26
Abstract
Abstract What is presented is a method for feature recognition using loose-gray-scale template matching including at least an initial point for locating within the received image a plurality of pixel points in gray-scale surrounding an initial point. The method has the steps of first locating the initial point and the plurality of pixel points to define feature boundaries and then generating a looseness interval about the located initial point with template information being associated therewith. The next step involves determining which one of a plurality of templates for fitting fits within a threshold looseness interval, and then outputting a signal associated with that recognized feature.
Inventors
- 👤 Robert P. Loce
- 👤 John C. Handley
- 👤 Clara Cuciurean-Zapan
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 12:39
CPC Classifications
H; H04; H04N; H04N1/00; H04N1/40; H04N1/40075; G; G06; G06V; G06V10/00; G06V10/20; G06V10/36; G06V10/40; G06V10/44; G06V10/443
Claims Analysis
Total Claims: 22
Claim 1
Independent Claim
296 words
A method for feature recognition using a method for matching a plurality of templates with a received image comprising: (a) receiving an image comprised of gray-scale image data; (b) generating a two-dimensional window of the received gray-scale image data, the two-dimensional window having a target gray-scale pixel and a plurality of surrounding gray-scale pixels, each gray-scale pixel being associated with a pixel of received gray-scale image data in the two-dimensional window; (c) determining a plurality of looseness intervals for one template of a plurality of templates, each looseness interval being a difference between a gray-scale pixel associated with the two-dimensional window and a template gray scale pixel from the template and corresponding to the pixel location in the two-dimensional window; (d) determining a template looseness interval value for the template based upon the determined plurality of looseness intervals; (e) comparing the determined template looseness interval value to a threshold looseness interval value, the threshold looseness interval value being a maximum allowable value for the determined template looseness interval value that indicates a loosely matched template; (f) determining, based upon the comparison between the determined template looseness interval value and the threshold looseness interval, which template of the plurality of templates loosely matches the two-dimensional window of received gray-scale image data, the loosely matched template being a template wherein the determined template looseness interval value associated therewith is equal to a non-zero value and the threshold looseness interval value is equal to a non-zero value, an exactly matched template being a template wherein the associated determined looseness interval value is equal to a zero value and the threshold looseness interval value is equal to a zero value; and (g) outputting an identifier associated with the loosely matched template such that the identifier indicates a recognized image feature.
Claim 2
Independent Claim
296 words
A method for feature recognition using a method for matching a plurality of templates with a received image comprising: (a) receiving an image comprised of gray-scale image data; (b) generating a two-dimensional window of the received gray-scale image data, the two-dimensional window having a target gray-scale pixel and a plurality of surrounding gray-scale pixels, each gray-scale pixel being associated with a pixel of received gray-scale image data in the two-dimensional window; (c) determining a plurality of looseness intervals for one template of a plurality of templates, each looseness interval being a difference between a gray-scale pixel associated with the two-dimensional window and a template gray scale pixel from the template and corresponding to the pixel location in the two-dimensional window; (d) determining a template looseness interval value for the template based upon the determined plurality of looseness intervals; (e) comparing the determined template looseness interval value to a threshold looseness interval value, the threshold looseness interval value being a maximum allowable value for the determined template looseness interval value that indicates a loosely matched template; (f) determining, based upon the comparison between the determined template looseness interval value and the threshold looseness interval, which template of the plurality of templates loosely matches the two-dimensional window of received gray-scale image data, the loosely matched template being a template wherein the determined template looseness interval value associated therewith is equal to a non-zero value and the threshold looseness interval value is equal to a non-zero value, an exactly matched template being a template wherein the associated determined looseness interval value is equal to a zero value and the threshold looseness interval value is equal to a zero value; and (g) outputting an identifier associated with the loosely matched template such that the identifier indicates a recognized image feature.
Claim 3
Dependent Claim
49 words
The method as in claim 1 , wherein the recognized image feature may a feature from a group of features including serifs, corners, vertical strokes, horizontal strokes, diagonal strokes, glyphs, platen cover marks, ink traps, paper edges, registration marks, binding holes and marks, or presence of adjacent color edges.
Claim 4
Dependent Claim
21 words
The method as in claim 1 , further comprising: (h) determining from a plurality of identifiers a recognized global image feature.
Claim 5
Dependent Claim
32 words
The method as in claim 3 , wherein the recognized global image feature may a global feature from a group of global features including financial documents, secure documents, bonds, stamps, or passports.
Claim 6
Dependent Claim
22 words
The method as in claim 1 , further comprising: (h) increasing the contrast of the recognized feature based upon the outputted identifier.
Claim 7
Dependent Claim
19 words
The method as in claim 1 , further comprising: (h) darkening the recognized feature based upon the outputted identifier.
Claim 8
Dependent Claim
19 words
The method as in claim 1 , further comprising: (h) thinning the recognized feature based upon the outputted identifier.
Claim 9
Dependent Claim
17 words
The method as in claim 1 , wherein the recognized image feature is an adjacent colored edged.
Claim 10
Dependent Claim
20 words
The method as in claim 1 , wherein the determined plurality of looseness intervals are determined from various color separations.
Claim 11
Dependent Claim
31 words
The method as claimed in claim 1 , wherein the determined looseness interval value for the template is the average looseness interval value determined from the plurality of determined looseness intervals.
Claim 12
Dependent Claim
37 words
The method as in claim 1 , further comprising: (h) using, when more than one template loosely matches the two-dimensional window of received gray-scale image data, an arbitration process to select the most preferred loosely matched template.
Claim 13
Dependent Claim
20 words
The method as in claim 1 , further comprising: (h) creating an image plane being composed of the outputted identifiers.
Claim 14
Independent Claim
221 words
A method for feature recognition using a method for matching a plurality of templates with a received image comprising: (a) receiving an image comprised of gray-scale image data; (b) generating a two-dimensional window of the received gray-scale image data, the two-dimensional window having a target gray-scale pixel and a plurality of surrounding gray-scale pixels, each gray-scale pixel being associated with a pixel of received gray-scale image data in the two-dimensional window; (c) determining a plurality of looseness intervals for one template from a plurality of templates, each looseness interval being a difference between a gray-scale pixel associated with the two-dimensional window and a template gray scale pixel from the template and corresponding to the pixel location in the two-dimensional window; (d) comparing each non-zero looseness interval of the plurality of looseness intervals with a threshold looseness interval to determine a looseness interval number for the template, the looseness interval number being equal to how many non-zero looseness intervals within the set of looseness intervals are less than the threshold looseness interval; (e) selecting the template of the plurality of templates having a greatest associated looseness interval number as the template that loosely matches the two-dimensional window of received gray-scale image data; and (f) outputting an identifier associated with the loosely matched template such that the identifier indicates a recognized image feature.
Claim 15
Independent Claim
221 words
A method for feature recognition using a method for matching a plurality of templates with a received image comprising: (a) receiving an image comprised of gray-scale image data; (b) generating a two-dimensional window of the received gray-scale image data, the two-dimensional window having a target gray-scale pixel and a plurality of surrounding gray-scale pixels, each gray-scale pixel being associated with a pixel of received gray-scale image data in the two-dimensional window; (c) determining a plurality of looseness intervals for one template from a plurality of templates, each looseness interval being a difference between a gray-scale pixel associated with the two-dimensional window and a template gray scale pixel from the template and corresponding to the pixel location in the two-dimensional window; (d) comparing each non-zero looseness interval of the plurality of looseness intervals with a threshold looseness interval to determine a looseness interval number for the template, the looseness interval number being equal to how many non-zero looseness intervals within the set of looseness intervals are less than the threshold looseness interval; (e) selecting the template of the plurality of templates having a greatest associated looseness interval number as the template that loosely matches the two-dimensional window of received gray-scale image data; and (f) outputting an identifier associated with the loosely matched template such that the identifier indicates a recognized image feature.
Claim 16
Dependent Claim
49 words
The method as in claim 13 , wherein the recognized image feature may a feature from a group of features including serifs, corners, vertical strokes, horizontal strokes, diagonal strokes, glyphs, platen cover marks, ink traps, paper edges, registration marks, binding holes and marks, or presence of adjacent color edges.
Claim 17
Dependent Claim
22 words
The method as in claim 13 , further comprising: (g) increasing the contrast of the recognized feature based upon the outputted identifier.
Claim 18
Dependent Claim
19 words
The method as in claim 13 , further comprising: (g) darkening the recognized feature based upon the outputted identifier.
Claim 19
Dependent Claim
19 words
The method as in claim 13 , further comprising: (g) thinning the recognized feature based upon the outputted identifier.
Claim 20
Dependent Claim
17 words
The method as in claim 13 , wherein the recognized image feature is an adjacent colored edged.
Claim 21
Dependent Claim
37 words
The method as in claim 13 , further comprising: (g) using, when more than one template loosely matches the two-dimensional window of received gray-scale image data, an arbitration process to select the most preferred loosely matched template.
Claim 22
Dependent Claim
20 words
The method as in claim 13 , further comprising: (g) creating an image plane being composed of the outputted identifiers.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2007-10-19 | 2008-04-19 | 2008-10-19 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2011-10-19 | 2012-04-19 | 2012-10-19 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2015-10-19 | 2016-04-19 | 2016-10-19 | $7700.00 | Window Closed / Lapsed |