Perform image segmentation and background removal in javascript using superpixes

An extensive evaluation and comparison of 28 state-of-the-art superpixel algorithms on 5 datasets.

Library containing 7 state-of-the-art superpixel algorithms with a total of 9 implementations used for evaluation purposes in [1] utilizing an extended version of the Berkeley Segmentation Benchmark.

20x Real-time superpixel SLIC Implementation with CPU

Tools used in [2] to pre-process the ground truth segmentations to evaluate superpixel algorithms.

Bachelor thesis "Superpixel Segmentation using Depth Information", including a thorough comparison of several state-of-the-art superpixel algorithms.

Lazy random walks for superpixel segmentation (IEEE TIP14)

Real-time Superpixel Segmentation by DBSCAN Clustering Algorithm (TIP16)

Converted datasets for davidstutz/superpixel-benchmark.

Interactive compariosn of superpixel algorithms as presented in the bachelor thesis "Superpixel Segmentation using Depth Information" [1].

GCPR 2015 paper and poster "Superpixel Segmentation: An Evaluation".

Image segmentation implemented in python