Implementation of Basic Digital Image Processing Tasks in Python / OpenCV
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Updated
Feb 20, 2019 - Python
Implementation of Basic Digital Image Processing Tasks in Python / OpenCV
Connected Component Labelling using 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
Topics learned and implemented as part of Computer Vision course
Code for Parallel Algorithms assignments (Fall 2019).
Computes graph connectivity for large graphs
An image processing library, including methods of filtering, object detection, noise reduction, etc
Extraction of connected components from the images with PGM file format using Otsu's thresholding and BFS/DFS methods
A generic, STL-like and image-agnostic C++ library for connected component labelling and feature extraction.
The implementation of algorithm Parallel graph component labelling with GPUs and CUDA.
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.
Demonstration of a few useful segmentation algorithms.
All assignments completed as a part of my Digital Image Processing Course
Final project in Parallel Computation
Implemented parallel and distributed algorithms using OpenMP, Apache Spark and NVIDIA CUDA
Practical activity #2, Data Structures, in Computer Engineering graduation.
JavaANPR: Automatic Number Plate Detection of Vehicle Images Based On Edge Detection and Morphological Approach
Matlab image processing programs without using built-in functions.
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