Examples#
A gallery of examples and that showcase how scikit-image can be used. Some examples demonstrate the use of the API in general and some demonstrate specific applications in tutorial form.
Hint
Check out our User guide for a narrative introduction to key library conventions and basic image manipulation.
Data#
Operations on NumPy arrays#
Using simple NumPy operations for manipulating images
Decompose flat footprints (structuring elements)
Manipulating exposure and color channels#
Separate colors in immunohistochemical staining
Edges and lines#
Geometrical transformations and registration#
Image registration#
Using Polar and Log-Polar Transformations for Registration
Filtering and restoration#
Removing small objects in grayscale images with a top hat filter
Band-pass filtering by Difference of Gaussians
Non-local means denoising for preserving textures
Full tutorial on calibrating Denoisers Using J-Invariance
Detection of features and objects#
Multi-Block Local Binary Pattern for texture classification
Gabors / Primary Visual Cortex “Simple Cells” from an Image
SIFT feature detector and descriptor extractor
Local Binary Pattern for texture classification
Segmentation of objects#
Region Boundary based Region adjacency graphs (RAGs)
Comparison of segmentation and superpixel algorithms
Explore and visualize region properties with pandas
Trainable segmentation using local features and random forests
Use rolling-ball algorithm for estimating background intensity
Longer examples and demonstrations#
Use pixel graphs to find an object’s geodesic center
Comparing edge-based and region-based segmentation
Face classification using Haar-like feature descriptor
Measure fluorescence intensity at the nuclear envelope
Examples for developers#
In this folder, we have examples for advanced topics, including detailed explanations of the inner workings of certain algorithms.
These examples require some basic knowledge of image processing. They are targeted at existing or would-be scikit-image developers wishing to develop their knowledge of image processing algorithms.
