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Schaefer et al., 2012 - Google Patents

Multi-dimensional local binary pattern descriptors for improved texture analysis

Schaefer et al., 2012

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Document ID
9752314123379450289
Author
Schaefer G
Doshi N
Publication year
Publication venue
Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)

External Links

Snippet

Texture analysis algorithms are employed in many computer vision applications. A group of high performing texture algorithms are based on the concept of local binary patterns (LBP) which describe the relationship of pixels to their local neighbourhood. LBP descriptors are …
Continue reading at projet.liris.cnrs.fr (PDF) (other versions)

Classifications

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    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/4671—Extracting features based on salient regional features, e.g. Scale Invariant Feature Transform [SIFT] keypoints
    • G06K9/4676—Extracting features based on a plurality of salient regional features, e.g. "bag of words"
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    • G06K9/4642—Extraction of features or characteristics of the image by performing operations within image blocks or by using histograms
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    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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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
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    • G06K9/6228—Selecting the most significant subset of features
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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
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