Texture classification by local rank correlation.

D. Harwood; M. Subbarao; L. S. Davis

doi:10.1016/0734-189X(85)90060-X

Texture classification by local rank correlation.

D. Harwood
, M. Subbarao
, L. S. Davis

Electrical Engineering

University of Maryland, College Park

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

Texture samples are classified based on their distribution of local statistics, either rank correlations or convolutions. This method achieves generally optimal classification rates. It appears to be robust because local order statistics are unaffected by local sample differences due to monotonic shifts of texture gray values and are less sensitive to noise.-after Authors

Original language	English
Pages (from-to)	404-411
Number of pages	8
Journal	Computer Vision, Graphics and Image Processing
Volume	32
Issue number	3
DOIs	https://doi.org/10.1016/0734-189X(85)90060-X
State	Published - 1985

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title = "Texture classification by local rank correlation.",

abstract = "Texture samples are classified based on their distribution of local statistics, either rank correlations or convolutions. This method achieves generally optimal classification rates. It appears to be robust because local order statistics are unaffected by local sample differences due to monotonic shifts of texture gray values and are less sensitive to noise.-after Authors",

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year = "1985",

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T1 - Texture classification by local rank correlation.

AU - Harwood, D.

AU - Subbarao, M.

AU - Davis, L. S.

PY - 1985

Y1 - 1985

N2 - Texture samples are classified based on their distribution of local statistics, either rank correlations or convolutions. This method achieves generally optimal classification rates. It appears to be robust because local order statistics are unaffected by local sample differences due to monotonic shifts of texture gray values and are less sensitive to noise.-after Authors

AB - Texture samples are classified based on their distribution of local statistics, either rank correlations or convolutions. This method achieves generally optimal classification rates. It appears to be robust because local order statistics are unaffected by local sample differences due to monotonic shifts of texture gray values and are less sensitive to noise.-after Authors

UR - https://www.scopus.com/pages/publications/0022267860

U2 - 10.1016/0734-189X(85)90060-X

DO - 10.1016/0734-189X(85)90060-X

M3 - Article

AN - SCOPUS:0022267860

SN - 0734-189X

VL - 32

SP - 404

EP - 411

JO - Computer Vision, Graphics and Image Processing

JF - Computer Vision, Graphics and Image Processing

IS - 3

ER -

Texture classification by local rank correlation.

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