There are 3 repositories under superpixel-segmentation topic.
Image segmentation - general superpixel segmentation & center detection & region growing
Superpixel Sampling Networks (ECCV2018)
Implementation of Image Processing Segmentation techniques and algorithms for Oil Spill detection in SAR images
Python implementation of LSC algorithm, (C) Zhengqin Li, Jiansheng Chen, 2014
Codes for our paper "Boundary-Enhanced Self-Supervised Learningfor Brain Structure Segmentation"
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
Matlab scripts that implement necessary algorithmic procedures to automatically color a black and white image. In particular, you need to develop code to perform some computing activities:
SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification, JSTARS, 2021
The work presented explains how to segment the brain tumour area in absence of interaction with user basing his technique on a saliency map constructed from three different resonance techniques.
Simple Linear Iterative Clustering C# .NET Framework
Segmentation network using superpixels and zoom-out features
Basic implementation of SLIC algorithm for generating superpixels.
Codes to compute Turbopixels/Turbovoxels and other related tools
Unofficial python implementation of the paper "Lazy Random Walks for Superpixel Segmentation"
HPCDL [Remote Sensing 2025]
FastSLIC implementation written in Rust
This repository, contains my academic work for the Fall 2023 CMSC828I course. It includes assignments, projects, and relevant documentation covering various aspects of computer vision and recognition.
SLIC (Simple Linear Iterative Clustering) Superpixels for pixel clustering and segmentation
A simple command line tool that uses K-Means clustering and SLIC segmentation to categorize pixels within an image into their respective clusters and super pixels
Image Segumnetation by Applying the Superpixel Algorithm to Images
Extract the medial axis and build a 3D mesh from images of the same object.
Github mirror of Alex Levinshtein's Turbopixels implementation
This project provides an interactive GUI tool to accelerate image annotation by leveraging superpixel segmentation. It uses SLIC-based superpixels to divide images into coherent regions, enabling users to label entire segments instead of individual pixels, significantly reducing annotation time and effort.
A pytorch implementation which builds a segmentation network which uses SLIC Superpixels as input.
Image Segmentation is the process of partitioning an image into multiple segments(superpixels). The goal is to represent the image as something that is easier to analyze. In other words, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.