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Zhao et al., 2020 - Google Patents

A novel Neutrosophic image segmentation based on improved fuzzy C-means algorithm (NIS-IFCM)

Zhao et al., 2020

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Document ID
17670817217333104145
Author
Zhao J
Wang X
Li M
Publication year
Publication venue
International journal of pattern recognition and artificial intelligence

External Links

Snippet

Image segmentation is a classical problem in the field of computer vision. Fuzzy c-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary …
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Classifications

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    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • 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
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    • G06K9/62—Methods or arrangements for recognition using electronic means
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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
    • G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
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    • G—PHYSICS
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    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
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    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
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