Mohamed Ghallab's starred repositories
Instabug-React-Native
In-app feedback and bug reporting tool for React Native
raisa-tasks
Solutions for raisa energy data engineering internship tasks
copilot-docs
Documentation for GitHub Copilot
filterpy
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian Filters in Python'.
Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Human-Detection-in-videos
Feature Extraction through hog, image histogram and spatial features, Used SVM classifier, Sliding window algorithm
Pedestrian-Detection-using-HOG-and-SVM
Pedestrian Detection using HOG Feature extraction and SVM Classifier.
python-hog
Python implementation of the Histogram of Oriented Gradients.
DigitalImageProcessing
Histogram Of Oriented Gradients Application for Pedestrian Detection
Pedestrian-detection
In the field of road safety, pedestrian detection remains an area of research despite the development it has known since its creation. A camera makes it possible to recover images which will be processed to detect the passage of pedestrians.
Image-Processing-Project
Car & Pedestrian Detection ,Counting circle & eclipse
Image-Processing-and-Feature-Detection
Image Processing and feature detection are the two most important areas of research in Computer Vision. In our one year project we successfully implemented various techniques and coding schemes that deal with Image Processing and Feature Detection of both still images and live video. We started with converting raw images to usable forms like grayscale, plots. We performed various mathematical operations on the image transforms like blurring (Gaussian Transform), sharpening etc. We learned to perform weighted addition of images. On both still images and live video features like face, eyes, smile, upper/lower body and full body have been detected. In this project, we used Haar Classifier to detect human face and eyes. We also employed the use of support vector machines to successfully identify human upper/lower and full body. The work can be extended to pedestrian detection in case of self-driving cars.