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US4764971A Expired / Lapsed US ⏱️ 0.0 years remaining

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
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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 07:40
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)

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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) 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

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