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2-D superpixel oversegmentation of images

`[`

computes
superpixels of the 2-D grayscale or RGB image `L`

,`NumLabels`

]
= superpixels(`A`

,`N`

)`A`

. `N`

specifies
the number of superpixels you want to create. The function returns `L`

,
a label matrix of type `double`

, and `NumLabels`

,
the actual number of superpixels that were computed.

The `superpixels`

function uses the simple
linear iterative clustering (SLIC) algorithm [1].
This algorithm groups pixels into regions with similar values. Using
these regions in image processing operations, such as segmentation,
can reduce the complexity of these operations.

`[`

computes superpixels of image `L`

,`NumLabels`

]
= superpixels(`A`

,`N`

,`Name,Value`

)`A`

using name-value pair arguments used to
control aspects of the segmentation.

[1] Radhakrishna Achanta, Appu Shaji, Kevin
Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, *SLIC
Superpixels Compared to State-of-the-art Superpixel Methods*.
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume
34, Issue 11, pp. 2274-2282, May 2012

`superpixels3`

| `boundarymask`

| `imoverlay`

| `label2idx`

| `label2rgb`