US4764971A
Expired / Lapsed
US
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Image processing method including image segmentation
Assignee: Eastman Kodak Co
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Filing: 1985-11-25
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Granted: 1988-08-16
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Expires: 2005-11-25
Abstract
Abstract An image signal is segmented into regions of constant contrast or variance by applying a generalized-likelihood-ratio test to an image difference signal.
Inventors
- 👤 James R. Sullivan
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-24 05:56
CPC Classifications
G; G06; G06T; G06T7/00; G06T7/10; G06T7/143; G06T7/12
Claims Analysis
Total Claims: 5
Claim 1
Independent Claim
208 words
An image processing method comprising the steps of: a. scanning an image to produce an image signal; b. digitizing the image signal to produce a digital image having pixel amplitude values; c. forming an image difference signal by taking differences between successive pixel amplitude values in the digital image; d. segmenting in segmenting means the digital image into segments of pixel amplitude values having constant variance by applying a generalized likelihood ratio test (GLRT) to the image difference signal, to identify image segments, the GLRT testing for the hypothesis H.sub.φ that a first set of image amplitude difference values from a Laplacian distribution having a variance σ are statistically similar to a second set of image amplitude difference values having a variance σ 0 to within a confidence level α, where α is an operator input value, the GLRT employing a generalized likelihood ratio (GLR) that is the ratio of maximum likelihood (ML) estimators for the variance σ of a distribution having a Laplacian probability density function (pdf); e. supplying said segments to image processing means; f. processing in said image processing means the digital image employing said identified image segments to produce a processed digital image; and g. supplying said processed digital image to an output device.
Claim 2
Dependent Claim
208 words
An image processing method comprising the steps of: a. scanning an image to produce an image signal; b. digitizing the image signal to produce a digital image having pixel amplitude values; c. forming an image difference signal by taking differences between successive pixel amplitude values in the digital image; d. segmenting in segmenting means the digital image into segments of pixel amplitude values having constant variance by applying a generalized likelihood ratio test (GLRT) to the image difference signal, to identify image segments, the GLRT testing for the hypothesis H.sub.φ that a first set of image amplitude difference values from a Laplacian distribution having a variance σ are statistically similar to a second set of image amplitude difference values having a variance σ 0 to within a confidence level α, where α is an operator input value, the GLRT employing a generalized likelihood ratio (GLR) that is the ratio of maximum likelihood (ML) estimators for the variance σ of a distribution having a Laplacian probability density function (pdf); e. supplying said segments to image processing means; f. processing in said image processing means the digital image employing said identified image segments to produce a processed digital image; and g. supplying said processed digital image to an output device.
Claim 3
Dependent Claim
173 words
The image processing method claimed in claim 1, wherein said step of segmenting further includes the steps of: a. estimating, in estimating means, the variance σ 0 of pixel amplitude difference values in a first segment of said image difference signal using the ML estimator; b. estimating, in estimating means, the variance σ of the pixel amplitude difference values in a second segment of said image difference signal, including the last pixel amplitude difference value of said first segment and the next adjacent pixel amplitude difference value using the ML estimator; c. applying said GLRT to σ and σ 0' , if the hypothesis H.sub.φ is true, adding the second segment to the first segment to generate a single segment, and repeating the steps of estimating, or if the hypothesis is false, identifying said first segment as an identified segment and beginning a new first segment with said next adjacent pixel amplitude difference value; and d. repeating said steps of estimating and applying said GLRT until said image difference signal is completely segmented.
Claim 4
Dependent Claim
38 words
The image processing method claimed in claim 2, wherein said ML estimator is of the form: ##EQU16## where ΔS i is the ith pixel difference value and n is the number of pixel difference values in a segment.
Claim 5
Dependent Claim
66 words
The image processing method claimed in claim 3, wherein said GLR test is of the form: reject the hypothesis H.sub.φ whenever σ/σ 0 ≦a or ≧b where a and b are predetermined functions of the confidence level α, defined by the following relationships: ##EQU17## where r is a dummy variable employed in the summation, and n is the number of pixel difference values in a segment.
References Cited (Backward Citations)
US3699446A
Cited
US3737855A
Cited
US4509195A
Cited
US4468703A
Cited
US4499486A
Cited
US4493106A
Cited
US4462081A
Cited
US4583114A
Cited
US4573167A
Cited
US4631735A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1991-08-16 | 1992-02-15 | 1992-08-16 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 1995-08-16 | 1996-02-15 | 1996-08-16 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 1999-08-16 | 2000-02-15 | 2000-08-16 | $7700.00 | Window Closed / Lapsed |