diff --git a/Examples/BasicFilters/IndexedToRGBExample.rst b/Examples/BasicFilters/IndexedToRGBExample.rst index a99740c8b7235955f59fcd1ea2bfc6e41a411777..b9c02e70cc8661bed202df3aecf4e2bb90196459 100644 --- a/Examples/BasicFilters/IndexedToRGBExample.rst +++ b/Examples/BasicFilters/IndexedToRGBExample.rst @@ -7,7 +7,7 @@ If such regions are easy to manipulate -- it is easier and faster to compare two than a RGB value -- it is different when it comes to displaying the results. Here we present a convient way to convert such indexed image to a color image. In -such conversion, it is important to ensure that neighborhood region, which are +such conversion, it is important to ensure that neighboring regions, which are likely to have consecutive number have easily dicernable colors. This is done randomly using a hash function by ``ScalarToRGBPixelFunctor``. diff --git a/Examples/BasicFilters/PrintableImageFilterExample.rst b/Examples/BasicFilters/PrintableImageFilterExample.rst index 532a2f51f1c8a7b14a520857cc06510324aac8a2..9c503fa73bb81e54857e01cdac3e3fce27c071bf 100644 --- a/Examples/BasicFilters/PrintableImageFilterExample.rst +++ b/Examples/BasicFilters/PrintableImageFilterExample.rst @@ -14,7 +14,7 @@ matching the red band with the red color, etc. Some satellites (SPOT 5 is an example) do not acquire all the visible spectral bands: the blue can be missing and replaced by some other wavelength of interest for a specific application. In these situations, another mapping has -to be created. That's why, the vegetation often appears in red in satellite +to be created. That's why the vegetation often appears in red in satellite images. The band order in the image products can be also quite tricky. It could be in