Patent US6181473B1 successfully refreshed from Google Patents.
Patent US6501857B1 successfully refreshed from Google Patents.
US6501857B1
Expired / Lapsed
US
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Method and system for detecting and classifying objects in an image
Assignee: Hewlett Packard Development Co LP
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Filing: 1999-07-20
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Granted: 2002-12-31
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Expires: 2019-07-20
Abstract
Abstract This disclosure provides a system for classifying images, used in image detection, image recognition, or other computer vision. The system processes directory images to obtain eigenvectors and eigenvalues, and selects a set of “smooth” basis vectors formed by linear combinations of these eigenvectors to be applied against a target image. Contrary to conventional wisdom, however, a group of the eigenvectors having the weakest eigenvalues are used to select the basis vectors. A second process is then performed on this group of “weakest” eigenvectors to identify a set of candidate vectors, ordered in terms of “smoothness.” The set of basis vectors (preferably 3-9) is then chosen from the candidate vectors in order of smoothness, which are then applied in an image detection or image recognition process. Unlike some conventional systems where “strong” directory presence and thresholds are used to detect possible matches, the present system uses smooth, weak vectors to ideally produce zero or near zero results for matches.
Inventors
- 👤 Craig Gotsman
- 👤 Daniel Keren
- 👤 Michael Elad
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-10 02:05
CPC Classifications
G; G06; G06V; G06V40/00; G06V40/10; G06V40/16; G06V40/161; G06F; G06F16/00; G06F16/50; G06F16/58; G06F16/583; Y; Y10; Y10S; Y10S707/00; Y10S707/99931; Y10S707/99933; Y10S707/99936
Claims Analysis
Total Claims: 16
Claim 1
Independent Claim
175 words
A method of classifying at least a portion of a target image using a digital processor and a directory of image examples, comprising: processing image examples with the digital processor and deriving therefrom a set of component vectors; using vectors from the set of component vectors to select a set of basis vectors; and using the digital processor to compare at least one vector in the set of basis vectors against the target image to yield comparison results and classifying portions of the target image in dependence upon the comparison results; wherein selecting basis vectors includes using the digital processor to perform an operation upon at least some vectors to determine strength of correlation with image examples in the directory, using the digital processor to perform an operation upon at least some vectors to determine strength of correlation with smooth image transitions, responsive to the operations, selecting the set of basis vectors to include only vectors which relatively weakly correlate with image examples in the directory and which relatively strongly correlate with smooth image transitions.
Claim 2
Independent Claim
175 words
A method of classifying at least a portion of a target image using a digital processor and a directory of image examples, comprising: processing image examples with the digital processor and deriving therefrom a set of component vectors; using vectors from the set of component vectors to select a set of basis vectors; and using the digital processor to compare at least one vector in the set of basis vectors against the target image to yield comparison results and classifying portions of the target image in dependence upon the comparison results; wherein selecting basis vectors includes using the digital processor to perform an operation upon at least some vectors to determine strength of correlation with image examples in the directory, using the digital processor to perform an operation upon at least some vectors to determine strength of correlation with smooth image transitions, responsive to the operations, selecting the set of basis vectors to include only vectors which relatively weakly correlate with image examples in the directory and which relatively strongly correlate with smooth image transitions.
Claim 3
Dependent Claim
86 words
A method according to claim 1 , wherein: processing image examples and deriving therefrom vectors includes determining eigenvectors for the directory of image examples; selecting the set of basis vectors includes performing a first cut of eigenvectors to eliminate eigenvectors that correlate by more than a predetermined amount with image examples; and selecting the set of basis vectors includes transforming remaining eigenvectors in a manner that emphasizes smoothness and selecting a specific number Y of transformed vectors in order of strongest correlation with smooth image transitions.
Claim 4
Dependent Claim
78 words
A method according to claim 1 , wherein selecting the set of basis vectors includes: applying a weighting function to vectors, the weighting function emphasizing both weakness of correlation with image examples and vector smoothness, to thereby develop a weight for each vector; and selecting the set of basis vectors includes using weights developed from application of the weighting function to identify a specific set of Y vectors associated with strongest weights from application of the weighting function.
Claim 5
Dependent Claim
106 words
A method according to claim 1 , wherein: processing image examples includes processing image examples and deriving therefrom eigenvectors; performing the operation upon vectors to determine which most strongly correlate with image examples in the directory includes determining values associated with each eigenvector, each value indicative of how strongly the associated eigenvector correlates with image examples in the directory, and selecting the set of basis vectors includes excluding from consideration at least those eigenvectors corresponding to at least the strongest fifty percent of correlation with image examples in the directory, and selecting a set of basis vectors dependent upon all remaining eigenvectors not excluded from consideration.
Claim 6
Dependent Claim
35 words
A method according to claim 4 , wherein selecting the set of basis vectors includes determining eigenvectors responsible for at least ninety-five percent of correlation with the image example, and excluding those eigenvectors from consideration.
Claim 7
Dependent Claim
35 words
A method according to claim 4 , wherein selecting the set of basis vectors includes determining eigenvectors responsible for at least ninety-nine percent of correlation with the image example, and excluding those eigenvectors from consideration.
Claim 8
Dependent Claim
112 words
A method according to claim 1 , further using one of software or firmware that implements a singular value decomposition function, and wherein selecting the set of basis vectors includes: applying the singular value decomposition function to image examples in the directory, to thereby generate a set of eigenvectors and a set of associated weights; selecting a group of eigenvectors having the smallest associated weights; applying a measure to at least those eigenvectors in the group using the digital processor, to thereby produce candidate vectors which have been processed to emphasize smoothness; and responsive to relative smoothness, choosing the set of basis vectors to be a set of Y smoothest candidate vectors.
Claim 9
Dependent Claim
65 words
A method according to claim 1 , wherein classifying portions of the target image includes: convolving the smoothest vector from the set of basis vectors with the target image, to thereby derive a set of products, wherein the set of products is characterized as being near zero substantially only for image regions representing likely presence of image examples from the directory in the target image.
Claim 10
Dependent Claim
46 words
A method according to claim 8 , further comprising: convolving the second smoothest vector from the set of basis vectors with the target image, at least in areas associated with near zero products as a result of convolution of the smoothest vector with the target image.
Claim 11
Dependent Claim
70 words
A method according to claim 1 , wherein: said method further comprises, during a training phase, determining mapping of each vector in the set of basis vectors to each image example in the directory; and classifying images includes image recognition, by recognizing a mapping between vectors in the set of basis vectors to the target image which is substantially similar to mapping between a specific image example in the directory.
Claim 12
Dependent Claim
34 words
A method according to claim 1 , wherein said method further comprises, during a training phase, selection of the set of basis vectors through a single minimization function having two singular value decomposition components.
Claim 13
Independent Claim
144 words
A method of classifying at least a portion of a target image using a digital processor and a directory of image examples, comprising: processing image examples with the digital processor and deriving therefrom a set of component vectors; using component vectors to select a set of basis vectors; and using the digital processor to compare at least one vector in the set of basis vectors against the target image to yield comparison results and classifying portions of the target image in dependence upon the comparison results; wherein selecting the set of basis vectors includes selecting only vectors for the set of basis vectors which, when convolved with portions of the target image having matches with a image example in the directory, tend to produce a result that is substantially zero and not selecting vectors that tend to produce a result that is substantially non-zero.
Claim 14
Independent Claim
144 words
A method of classifying at least a portion of a target image using a digital processor and a directory of image examples, comprising: processing image examples with the digital processor and deriving therefrom a set of component vectors; using component vectors to select a set of basis vectors; and using the digital processor to compare at least one vector in the set of basis vectors against the target image to yield comparison results and classifying portions of the target image in dependence upon the comparison results; wherein selecting the set of basis vectors includes selecting only vectors for the set of basis vectors which, when convolved with portions of the target image having matches with a image example in the directory, tend to produce a result that is substantially zero and not selecting vectors that tend to produce a result that is substantially non-zero.
Claim 15
Dependent Claim
113 words
A method according to claim 12 , wherein the digital processor includes a computer and a memory device, further comprising: inputting a set of image examples which are formed by two-dimensional pixel arrays to the computer, such that the image examples are represented by matrices; processing the matrices with the computer to realize an orthogonal linear subspace of the matrices, defined by eigenvectors that weakly correlate with the image examples; using the computer to identify smooth vectors from the orthogonal linear subspace and to select from the smooth vectors a set of smoothest vectors; and outputting the set of smoothest vectors to a memory device for use as the set of basis vectors.
Claim 16
Dependent Claim
49 words
A method according to claim 13 , wherein processing the matrices is performed by: applying a singular value decomposition function to the matrices, to yield eigenvectors and associated eigenvalues; and selecting a group of eigenvectors having the smallest eigenvalues, the orthogonal linear subspace identified by the selected group of
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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) | 2005-12-30 | 2006-07-01 | 2006-12-31 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2009-12-30 | 2010-07-01 | 2010-12-31 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2013-12-30 | 2014-07-01 | 2014-12-31 | $7700.00 | Window Closed / Lapsed |