recolorize: color-based image segmentation (for people with other things to do) | Quick start | Batch processing | The recolorize class | General guidelines | This is a lot of options. How do I choose a procedure? | Can you define an optimality condition to do all the segmentation automatically? | What about batch processing?

absorbLayer | editLayer/editLayers | mergeLayers

Quick version | Defining your question(s) | Image acquisition | Image preparation | Background masking

Reading an image into R

The recolorize function | Color spaces | Clustering methods | Number of clusters | imposeColors()

recluster() and recolorize2() | thresholdRecolor()

Visualizations | imDist | splitByColor | Exporting | Exporting to images | pavo package | patternize

Introduction | Earth Mover's Distance | $\chi^{2}$ Distance | Color Distance | Weighted Pairs Distance | Choosing a distance metric | Cheatsheet

Introduction | Color spaces in colordistance | RGB | HSV | CIELAB | Why does the choice of color space matter? | Choosing a color space for analysis | Scientific merit | Type of comparison | Ease-of-use | Further reading

Acquiring and prepping images | Loading the images into R | Binning the pixels | Comparing color histograms | Visualizing results | Exporting data | Shortcut | References

Introduction | Why binning? | Note: any valid filepath will work; this line is specific to | example data that comes with the package | Histogram method | K-means method | Choosing a binning method & parameters | Cheatsheet

What does this package do? | How it works | Rectangular ranges | Spherical ranges | Example of basic workflow | Finding the right color range | Counting the pixels | Using countColors | Multiple images | Questions or feedback?

Acquiring and prepping images | Determining the percentage of P. destructans fluorescence

Choosing a reference white | Converting from RGB to CIELab | Clustering in CIELab space | Distance matrices