felzenszwalb

felzenszwalb

skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20) [source]

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