General examples¶
General-purpose and introductory examples for scikit-image.
The narrative documentation introduces conventions and basic image manipulations.
Data¶
Operations on NumPy arrays¶
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Using simple NumPy operations for manipulating images
Manipulating exposure and color channels¶
Edges and lines¶
Geometrical transformations and registration¶
Image registration¶
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Using Polar and Log-Polar Transformations for Registration
Filtering and restoration¶
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Removing small objects in grayscale images with a top hat filter
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Full tutorial on calibrating Denoisers Using J-Invariance
Detection of features and objects¶
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Multi-Block Local Binary Pattern for texture classification
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Gabors / Primary Visual Cortex “Simple Cells” from an Image
Segmentation of objects¶
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Comparison of segmentation and superpixel algorithms
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Explore and visualize region properties with pandas
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Trainable segmentation using local features and random forests
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Use rolling-ball algorithm for estimating background intensity
Longer examples and demonstrations¶
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Use pixel graphs to find an object’s geodesic center
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Comparing edge-based and region-based segmentation
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Measure fluorescence intensity at the nuclear envelope
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Face classification using Haar-like feature descriptor
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.