morphological-operations

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

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.

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).

Classical computer-vision project for traffic-sign recognition with OCR, euro-coin detection and value estimation, and Watershed-based object counting using OpenCV and K-Means.

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

Two computer vision pipelines built in Python/OpenCV to detect, track, and estimate the speed of vehicles on a highway, and classify Lego bricks by shape.

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.

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

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

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.

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

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.

Classical CT segmentation: automatic threshold-based bone/skeleton segmentation with connectivity analysis and KD-tree component bridging, plus liver segmentation via anatomically-guided multi-seed region growing (Dice 0.89–0.94).

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.

Medical Image Segmentation and Anatomical Measurement Extraction with MATLAB & Python.