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

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.

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

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.

Topics learned and implemented as part of Computer Vision course

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

Code for Parallel Algorithms assignments (Fall 2019).

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