felzenszwalb(image, scale=1, sigma=0.8, min_size=20, multichannel=True)

Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter scale sets an observation level. Higher scale means less and larger segments. sigma is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation.

The number of produced segments as well as their size can only be controlled indirectly through scale . Segment size within an image can vary greatly depending on local contrast.

For RGB images, the algorithm uses the euclidean distance between pixels in color space.

Notes

The k parameter used in the original paper renamed to :None:None:`scale` here.

Parameters

image : (width, height, 3) or (width, height) ndarray

Input image.

scale : float

Free parameter. Higher means larger clusters.

sigma : float

Width (standard deviation) of Gaussian kernel used in preprocessing.

min_size : int

Minimum component size. Enforced using postprocessing.

multichannel : bool, optional (default: True)

Whether the last axis of the image is to be interpreted as multiple channels. A value of False, for a 3D image, is not currently supported.

Returns

segment_mask : (width, height) ndarray

Integer mask indicating segment labels.

Computes Felsenszwalb's efficient graph based image segmentation.

Examples

This example is valid syntax, but we were not able to check execution

>>> from skimage.segmentation import felzenszwalb
... from skimage.data import coffee
... img = coffee()
... segments = felzenszwalb(img, scale=3.0, sigma=0.95, min_size=5)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.segmentation._felzenszwalb.felzenszwalb

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