2012
DOI: 10.1016/j.ins.2011.10.014
|View full text |Cite
|
Sign up to set email alerts
|

Local multiple patterns based multiresolution gray-scale and rotation invariant texture classification

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
20
0

Year Published

2014
2014
2024
2024

Publication Types

Select...
7
1

Relationship

0
8

Authors

Journals

citations
Cited by 62 publications
(21 citation statements)
references
References 33 publications
0
20
0
Order By: Relevance
“…Among them, several features were used to recognize coin images [3,7,8,10,14]. Representatively, rotation-invariant local binary patterns were proposed [15][16][17][18]. These features could be robust against rotation by grouping the uniform patterns [15][16][17] or applying Fourier transform [18], and were applied to image-based by [10,14].…”
Section: Comparing With Previous Approachesmentioning
confidence: 99%
See 2 more Smart Citations
“…Among them, several features were used to recognize coin images [3,7,8,10,14]. Representatively, rotation-invariant local binary patterns were proposed [15][16][17][18]. These features could be robust against rotation by grouping the uniform patterns [15][16][17] or applying Fourier transform [18], and were applied to image-based by [10,14].…”
Section: Comparing With Previous Approachesmentioning
confidence: 99%
“…Representatively, rotation-invariant local binary patterns were proposed [15][16][17][18]. These features could be robust against rotation by grouping the uniform patterns [15][16][17] or applying Fourier transform [18], and were applied to image-based by [10,14]. Also, CSGabor was designed to be robust to rotation [3].…”
Section: Comparing With Previous Approachesmentioning
confidence: 99%
See 1 more Smart Citation
“…An extension of LBP is LMP from binary patterns to multiple patterns made to be more robust for image analysis, including the analysis of flat image areas (Zhu & Wang, 2012). Number of middle patterns between 0 and 1 are added which led LMP more informative than LBP.…”
Section: Local Multiple Pattern (Lmp)mentioning
confidence: 99%
“…It started by the revolutionary approach derived by Ojala et alto derive texture features by quantizing the local pixel values of a neighborhood in to two values and named it as local binary patterns (LBPs) [11,12]. Later several authors [13][14][15][16][17][18][19] carried out abundant work and derived efficient methods to further extend the benefits of LBP in various applications. The Binary features [12,13,15,20,21,22] gained reputation and recognition due to their efficient design, computational simplicity and good performance.…”
mentioning
confidence: 99%