An extensive evaluation and comparison of 28 state-of-the-art superpixel algorithms on 5 datasets.
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Updated
Jan 6, 2024 - C++
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
Learning Superpixels with Segmentation-Aware Affinity Loss
Implementation of the superpixel algorithm called SEEDS [1].
Implementation of efficient graph-based image segmentation as proposed by Felzenswalb and Huttenlocher [1] that can be used to generate oversegmentations.
Extended version of the Berkeley Segmentation Benchmark [1] used for evaluation in [2].
Superpixels segmentation algorithms with QT and OpenCV, with a nice GUI to colorize the cells
Invariant Superpixel Features for Object Detection
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
Example of using VLFeat's SLIC implementation from C++.
Real-time Superpixel Segmentation by DBSCAN Clustering Algorithm (TIP16)
Superpixel and Supervoxel computing
Image segmentation method for the generation of superpixels called waterpixels which follow an image contours.
Codes to compute Turbopixels/Turbovoxels and other related tools
Task at UIIP NASB
Implementation of simplified version of SLIC Superpixels algorithm
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