morphological-operations

[MIDL 2022 Oral] Learning Morphological Feature Perturbations for Calibrated Semi Supervised Segmentation

Morphological image processing (erosion, dilation, opening, closing, boundary extraction, region filling) with OpenCV — includes a CLI script and a step-by-step Colab notebook.

Extracting the structural skeleton of images using morphological operations.

Image Processing Algorithms

Brain tumor segmentation using unsupervised methods (K means++ clustering) with morphology operation for postprocessing

Image processing algorithms implemented from scratch with a PyQt5 GUI (no ready-made CV functions).

A lightweight, heuristic-based algorithm for segmenting characters in Iranian license plates using OpenCV. Features robust handling of shadows, noise, and connected characters without Deep Learning, achieving 98.68% accuracy.

Small separated projects to apply the image proccessing concepts on real world cases

This repository contains all the assignments I worked on as a part of the seminar for the course "Medical Visualization" from my Master's degree.

Coding solutions to multiple image processing problems like distance calculation, noise, contrast, and compression using different techniques like Distance Transform, Low-Pass Filters, Morphological Operators, and LZW Compression.

A computer vision pipeline implementing and comparing static and dynamic background subtraction techniques to isolate and track moving objects in video streams. Markdown

OpenCV-based Code 11 barcode decoder that detects rotation, removes noise, adjusts contrast, isolates barcode regions, and decodes bar patterns from challenging images.

Morphological operations in image processing using opencv

📸 A comprehensive toolkit for Computer Vision mastery using OpenCV and Python. This repository features 28+ modular implementations of image processing techniques, feature detection algorithms, and real-time video analysis tools (including Lane Detection & Invisible Cloak), all accessible through a custom-built, interactive Tkinter GUI.

Python-based Car License Plate Detection and OCR pipeline using Computer Vision (Morphology) and Machine Learning (HOG + SVM).

Structured implementations of classical computer vision primitives in MATLAB, covering filtering, frequency-domain analysis, wavelets, morphology, registration, and texture modeling with reproducible export-first design.

Complete Python pipeline for detecting horizontal and vertical boards in 16-bit TIFF images. Uses OpenCV and NumPy for edge detection, line extraction, board grouping, gap filling, and generates labeled visualizations. Optimized for grayscale industrial images and easily adjustable for custom parameters.

Hazır görüntü işleme kütüphanesi kullanmadan, tüm algoritmaları NumPy ile sıfırdan yazılmış Tkinter tabanlı masaüstü görüntü işleme uygulaması

OMR Sheet Evaluation system using Python and OpenCV. Automatically detects answer bubbles, evaluates marked responses, calculates scores, and visualizes grading results. Built with Computer Vision techniques including contour detection, thresholding, morphology, and pixel-density analysis for automated exam assessment.