jSLIC - superpixels

We made a Java-based open source implementation jSLIC - the superpixel clustering with better performance than the original Simple Linear Iterative Clustering. For more info, please see Fiji wiki.

As you can see the Interface to the plugin contains a parameters for superpixel configuration and also its final visualisation.

For the configuration there are only two parameters to be set:

• Init. grid size - in general it can be seen as an average superpixels size.
• Regularisation - influence the compactness of estimated superpixels. The range is from 0 (very elastic superpixels) to 1 (superpixels are nearly squares). Experimentally, we set as optimal value 0.2 for most cases.

To show of handle segmentation results we presented a few approaches:

• Overlap contours - simply draw the contours on resulting superpixels into the image by chosen colour.
• Export segments as ROIs - all superpixels are exported as polygons into the ROI Manager.
• Show final segmentation - add one more stack and fill each superpixel by a random colour.
• Save segmentation into file - export the superpixel segmentation into a text file as segmentation matrix with labels.

Borovec, J., & Kybic, J. (2014). jSLIC : superpixels in ImageJ. Computer Vision Winter Workshop. Praha.