Mogrify transforms an image or a sequence of images. These transforms
include image scaling, image rotation, color reduction, and others. The
transmogrified image overwrites the original image, unless an option such as
-format causes the output filename to be different from the input
filename.
The graphics formats supported by mogrify are listed in
ImageMagick(1).
In this example, '-size 120x120' gives a hint to the JPEG decoder
that the images are going to be downscaled to 120x120, allowing it to run
faster by avoiding returning full-resolution images to ImageMagick for
the subsequent resizing operation. The
'-resize 120x120' specifies the desired dimensions of the
output images. It will be scaled so its largest dimension is 120 pixels. The
'+profile "*"' removes any ICM, EXIF, IPTC, or other profiles
that might be present in the input and aren't needed in the thumbnails.
To scale an image of a cockatoo to exactly 640 pixels in width and 480
pixels in height, use:
Options are processed in command line order. Any option you specify on
the command line remains in effect for the set of images that follows,
until the set is terminated by the appearance of any option or -noop.
For a more detailed description of each option, see
ImageMagick(1).
Use -segment to segment an image by analyzing the histograms of
the color components and identifying units that are homogeneous with the
fuzzy c-means technique. The scale-space filter analyzes the histograms
of the three color components of the image and identifies a set of classes.
The extents of each class is used to coarsely segment the image with thresholding.
The color associated with each class is determined by the mean color of
all pixels within the extents of a particular class. Finally, any unclassified
pixels are assigned to the closest class with the fuzzy c-means technique.
The fuzzy c-Means algorithm can be summarized as follows:
Build a histogram, one for each color component of the image.
For each histogram, successively apply the scale-space filter and build
an interval tree of zero crossings in the second derivative at each scale.
Analyze this scale-space "fingerprint" to determine which peaks or valleys
in the histogram are most predominant.
The fingerprint defines intervals on the axis of the histogram. Each interval
contains either a minima or a maxima in the original signal. If each color
component lies within the maxima interval, that pixel is considered "classified"
and is assigned an unique class number.
Any pixel that fails to be classified in the above thresholding pass is
classified using the fuzzy c-Means technique. It is assigned to one of
the classes discovered in the histogram analysis phase.
The fuzzy c-Means technique attempts to cluster a pixel by finding the
local minima of the generalized within group sum of squared error objective
function. A pixel is assigned to the closest class of which the fuzzy membership
has a maximum value.
For additional information see:
Young Won Lim, Sang Uk Lee, ``On The Color Image Segmentation
Algorithm Based on the Thresholding and the Fuzzy c-Means Techniques'',
Pattern Recognition, Volume 23, Number 9, pages 935-952, 1990.
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