# Introduction

scikit-image is a collection of algorithms for image processing. It is
available *free of charge and free of restriction*. The project is run
*by the community, for the community*, and adheres to established
standards for Python scientific software development, such as *code
peer-review*, *inline API documentation*, and thorough *unit testing*.

scikit-image is built on top of *numpy*, *scipy*, and *cython*.

A full overview of functionality is available as
[API documentation](http://scikit-image.org/docs/stable/api/api.html).

Usage example are given in our
[gallery](http://scikit-image.org/docs/dev/auto_examples/).

For a more in-depth overview, take a look at [our paper](https://doi.org/10.7717/peerj.453).

Let's take a look at some
[real-world use-cases](https://docs.google.com/presentation/d/1hzjKWUAL8TGP8R7wkh_aKIjiij1FNy7Tamu1wrILVbM/edit?usp=sharing).

For the tutorial, ensure you've followed the
[installation instructions](./0_preparation.html).