Implementation of Basic Digital Image Processing Tasks in Python / OpenCV

A Connected Component Labelling algorithm implemented in CUDA

This repository contains the implementation of an Object Detection and Classification & Line and Circle Detection Application

The implementation of algorithm Parallel graph component labelling with GPUs and CUDA.

Computes graph connectivity for large graphs

Demonstration of a few useful segmentation algorithms.

An image processing library, including methods of filtering, object detection, noise reduction, etc

A generic, STL-like and image-agnostic C++ library for connected component labelling and feature extraction.

Connected Component Labelling using opencv

Connected Component Labelling

Extraction of connected components from the images with PGM file format using Otsu's thresholding and BFS/DFS methods

Matlab image processing programs without using built-in functions.

Data Structures: Arrays, Stacks, Queues, Graphs applications in image processing, tag parsing and routes/maps respectively.

App which solves the puzzles from the game Flow Free!

This repository is a collection of fundamental digital image processing operations and algorithms performed on greyscale images, or Portable Grey Map (PGM) files, using different data structures in C++, as part of an assignment and final project module for the Data Structures (CS2001) course.

All assignments completed as a part of my Digital Image Processing Course

Code for Parallel Algorithms assignments (Fall 2019).

Topics learned and implemented as part of Computer Vision course

Practical activity #2, Data Structures, in Computer Engineering graduation.