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Fukumi et al., 1994 - Google Patents

Rotation-invariant neural pattern recognition systems with application to coin recognition

Fukumi et al., 1994

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Document ID
12687712922946431902
Author
Fukumi M
Omatu S
Takeda F
Kosaka T
Publication year
Publication venue
J. SICE

External Links

Snippet

Humans can recognize any pattern easily even if it is transformed by scale-change, translation, rotation, and noise. However it is difficult for digital computers to recognize such patterns. In this background, artificial neural networks, which are models emulating a …
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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
    • G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
    • G06K9/4619—Biologically-inspired filters, e.g. receptive fields
    • G06K9/4623—Biologically-inspired filters, e.g. receptive fields with interaction between the responses of different filters
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    • G06N3/0675—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using optical means using electro-optical, acousto-optical or opto-electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
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    • 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
    • G06K9/6271—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 based on distances to prototypes
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    • G06N99/00—Subject matter not provided for in other groups of this subclass

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