felzenszwalb
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skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20)
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Computes Felsenszwalb’s efficient graph based image segmentation.
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 computes a separate segmentation for each channel and then combines these. The combined segmentation is the intersection of the separate segmentations on the color channels.
Parameters: image : (width, height, 3) or (width, height) ndarray
Input image.
scale : float
Free parameter. Higher means larger clusters.
sigma : float
Width of Gaussian kernel used in preprocessing.
min_size : int
Minimum component size. Enforced using postprocessing.
Returns: segment_mask : (width, height) ndarray
Integer mask indicating segment labels.
References
[R348] Efficient graph-based image segmentation, Felzenszwalb, P.F. and Huttenlocher, D.P. International Journal of Computer Vision, 2004
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