Method for indexing and retrieving manufacturing-specific digital imagery based on image content
Abstract
A method for indexing and retrieving manufacturing-specific digital images based on image content comprises three steps. First, at least one feature vector can be extracted from a manufacturing-specific digital image stored in an image database. In particular, each extracted feature vector corresponds to a particular characteristic of the manufacturing-specific digital image, for instance, a digital image modality and overall characteristic, a substrate/background characteristic, and an anomaly/defect characteristic. Notably, the extracting step includes generating a defect mask using a detection process. Second, using an unsupervised clustering method, each extracted feature vector can be indexed in a hierarchical search tree. Third, a manufacturing-specific digital image associated with a feature vector stored in the hierarchicial search tree can be retrieved, wherein the manufacturing-specific digital image has image content comparably related to the image content of the query image. More particularly, can include two data reductions, the first performed based upon a query vector extracted from a query image. Subsequently, a user can select relevant images resulting from the first data reduction. From the selection, a prototype vector can be calculated, from which a second-level data reduction can be performed. The second-level data reduction can result in a subset of feature vectors comparable tomore »
- Inventors:
- Issue Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1174907
- Patent Number(s):
- 6751343
- Application Number:
- 09/399,394
- Assignee:
- UT-Battelle, LLC (Oak Ridge, TN)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
- DOE Contract Number:
- AC05-96OR22464
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION
Citation Formats
Ferrell, Regina K., Karnowski, Thomas P., and Tobin, Jr., Kenneth W. Method for indexing and retrieving manufacturing-specific digital imagery based on image content. United States: N. p., 2004.
Web.
Ferrell, Regina K., Karnowski, Thomas P., & Tobin, Jr., Kenneth W. Method for indexing and retrieving manufacturing-specific digital imagery based on image content. United States.
Ferrell, Regina K., Karnowski, Thomas P., and Tobin, Jr., Kenneth W. Tue .
"Method for indexing and retrieving manufacturing-specific digital imagery based on image content". United States. https://www.osti.gov/servlets/purl/1174907.
@article{osti_1174907,
title = {Method for indexing and retrieving manufacturing-specific digital imagery based on image content},
author = {Ferrell, Regina K. and Karnowski, Thomas P. and Tobin, Jr., Kenneth W.},
abstractNote = {A method for indexing and retrieving manufacturing-specific digital images based on image content comprises three steps. First, at least one feature vector can be extracted from a manufacturing-specific digital image stored in an image database. In particular, each extracted feature vector corresponds to a particular characteristic of the manufacturing-specific digital image, for instance, a digital image modality and overall characteristic, a substrate/background characteristic, and an anomaly/defect characteristic. Notably, the extracting step includes generating a defect mask using a detection process. Second, using an unsupervised clustering method, each extracted feature vector can be indexed in a hierarchical search tree. Third, a manufacturing-specific digital image associated with a feature vector stored in the hierarchicial search tree can be retrieved, wherein the manufacturing-specific digital image has image content comparably related to the image content of the query image. More particularly, can include two data reductions, the first performed based upon a query vector extracted from a query image. Subsequently, a user can select relevant images resulting from the first data reduction. From the selection, a prototype vector can be calculated, from which a second-level data reduction can be performed. The second-level data reduction can result in a subset of feature vectors comparable to the prototype vector, and further comparable to the query vector. An additional fourth step can include managing the hierarchical search tree by substituting a vector average for several redundant feature vectors encapsulated by nodes in the hierarchical search tree.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jun 15 00:00:00 EDT 2004},
month = {Tue Jun 15 00:00:00 EDT 2004}
}
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