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Yuan et al., 2014 - Google Patents

Real-time keypoint recognition using restricted Boltzmann machine

Yuan et al., 2014

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Document ID
7456508219367065974
Author
Yuan M
Tang H
Li H
Publication year
Publication venue
IEEE transactions on neural networks and learning systems

External Links

Snippet

Feature point recognition is a key component in many vision-based applications, such as vision-based robot navigation, object recognition and classification, image-based modeling, and augmented reality. Real-time performance and high recognition rates are of crucial …
Continue reading at citeseerx.ist.psu.edu (PDF) (other versions)

Classifications

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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G06K9/6203—Shifting or otherwise transforming the patterns to accommodate for positional errors
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    • 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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    • G06K9/62—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
    • G06K9/627—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
    • G—PHYSICS
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    • 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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    • G06K9/00355—Recognition of hand or arm movements, e.g. recognition of deaf sign language
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