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Timm et al., 2011 - Google Patents

Non-parametric texture defect detection using Weibull features

Timm et al., 2011

Document ID
382790037237215556
Author
Timm F
Barth E
Publication year
Publication venue
Image Processing: Machine Vision Applications IV

External Links

Snippet

The detection of abnormalities is a very challenging problem in computer vision, especially if these abnormalities must be detected in images of textured surfaces such as textile, stone, or wood. We propose a novel, non-parametric approach for defect detection in textures that …
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Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
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    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
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    • G06K9/6228—Selecting the most significant subset of features
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    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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