MATLAB implementation to segment breast lesions in ultrasound images (ICIAR 2016)

Official code for "DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut", NeurIPS 2024

Variational Fair clustering

Image Segmentation using k-means, n-cuts and superpixels

A Source Camera Identification (SCI) system in Matlab

Image segmentation various methods

Official implementation of the paper "AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D Scans"

Segmentation based on similarity measure including Intensity difference and Distance of pixels. Also effect of rotation and addition of gaussian noise on segmentation is visualized using Matplotlib.

Image Segmentation on the Berkeley Segmentation Benchmark

Implementation of Fundamental Image Processing Techniques

CS421: Data-Mining Course, Faculty of Engineering, Alexandria University

An attempt at the network anomaly detection task using manually implemented k-means, spectral clustering and DBSCAN algorithms, with manually implemented evaluation metrics (precision, recall, f1-score and conditional entropy) used to evaluate these algorithms.

Scripts for the paper: A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data.

Scripts for the paper: A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data.

A summative coursework for CSC8628 Image Informatics

This project compares between different clustering algorithms: K-Means, Normalized Cut and DBSCAN algorithms for network anomaly detection on the KDD Cup 1999 dataset