plot_multi_images

Two 'Gray Level Co-occurrence Matrix' ('GLCM') implementations are included: The first is a fast 'GLCM' feature texture computation based on 'Python' 'Numpy' arrays ('Github' Repository, <https://github.com/tzm030329/GLCM>). The second is a fast 'GLCM' 'RcppArmadillo' implementation which is parallelized (using 'OpenMP') with the option to return all 'GLCM' features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, <doi:10.1109/ICIP.2019.8803652>.

Lampros Mouselimis

fastGLCM

'GLCM' Texture Features

Takahiro Toizumi

plot_multi_images function

<dl><dt>list_images</dt>
<dd>a list of images that should be visualized</dd>
<dt>par_ROWS</dt>
<dd>an integer specifying the number of rows of the ouput plot-grid</dd>
<dt>par_COLS</dt>
<dd>an integer specifying the number of columns of the output plot-grid</dd>
<dt>...</dt>
<dd>further arguments for the 'plot_multi_images' method of the 'GaborFeatureExtract' R6 class ('OpenImageR' package)</dd></dl>

Arguments

Plot multiple images — plot_multi_images

<dl>

<dt>list_images</dt>
<dd>a list of images that should be visualized</dd>


<dt>par_ROWS</dt>
<dd>an integer specifying the number of rows of the ouput plot-grid</dd>


<dt>par_COLS</dt>
<dd>an integer specifying the number of columns of the output plot-grid</dd>


<dt>...</dt>
<dd>further arguments for the 'plot_multi_images' method of the 'GaborFeatureExtract' R6 class ('OpenImageR' package)</dd>

</dl>

plot_multi_images: Plot multiple images

Description

Usage

Value

Arguments

Details