BSLIC: SLIC Superpixels Based on Boundary Term

BSLIC: SLIC Superpixels Based on Boundary Term

A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to imp...

SLIC Superpixels

Superpixels are becoming increasingly popular for use in computer vision applications. However, there are few algorithms that output a desired number of regular, compact superpixels with a low computational overhead. We introduce a novel algorithm that clusters pixels in the combined five-dimensional color and image plane space to efficiently generate compact, nearly uniform superpixels. The si...

Shape Based Outlier Detection in SLIC Superpixels

Segmentation Improvement of High Resolution Remote Sensing Images based on superpixels using Edge-based SLIC algorithm (E-SLIC)

The segmentation of high resolution remote sensing images is one of the most important analyses that play a significant role in the maximal and exact extraction of information.  There are different types of segmentation methods among which using  superpixels is one of the most important ones. Several methods have been proposed for extracting superpixels. Among the most successful ones, we can r...

gSLICr: SLIC superpixels at over 250Hz

We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to 83× from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.