Real Time Sign Language Detection Using ESP32-CAM
Computer Vision, Object Tracking, 2024
This project focuses on real-time sign language detection, utilizing an ESP32-CAM board for video capturing.
Projects
Hand Gesture-based Mouse Control
Computer Vision, Object Tracking, 2024
This project uses CVZONE to enable mouse control through hand gestures. The system tracks the tip of the index finger to move the mouse pointer on the screen. A “click” action is triggered when the thumb touches the tip of the index finger, simulating a mouse click. This project highlights the integration of hand gesture recognition with interactive computing. The code is available for exploration in this GitHub repository.
Hand Gesture-based Volume Control
Computer Vision, Object Tracking, 2024
This project leverages CVZONE to detect a hand and recognize finger gestures for controlling volume. By tracking the thumb and index finger, the system adjusts the volume: closing the thumb and index finger lowers the volume, while opening them increases it. This interactive application showcases real-time hand gesture recognition for practical control mechanisms. The code is available for exploration in this GitHub repository.
Eye tracker
Computer Vision, Object Tracking, 2024
In this project, CVZONE is used to detect and draw borders around a face, followed by the application of a mesh detector with a limit of detecting one face (maximum faces = 1).
Image Segmentation for Person, Clothing, and Accessories Using U-Net
Computer Vision, Semantic Segmentation, 2024
This project involves a U-Net model trained on a Kaggle dataset to segment various elements in an image, including persons, clothing, accessories, hats, and more.
Instance Segmenation_balloons
Computer Vision, Instance Segmentation, 2024
This project involves training a custom dataset using Facebook Research’s Detectron2 framework, specifically with the Mask R-CNN model.
Subtitle translator
Natural Language Processing, NLP, 2024
This code automatically translates English subtitles into a specified target language.
Virtual environment segmentation
Computer Vision, Semantic Segmentation, 2024
This project, based on Stanford’s computer vision course, utilizes the KITTI dataset—a collection of images and LIDAR data for autonomous driving research.
Geospatial vehicle detection
Computer Vision, Object Detection, 2024
This project involves training a pretrained RetinaNet model on the NWPU-VHR-10 dataset, which contains high-resolution aerial images for object detection tasks.
Detecting car and its model
Computer Vision, Object Detection, 2024
This project involves training multiple models on Stanford’s car dataset to classify various car types.
Soft exudates segmentation
Computer Vision, Segmentation, 2024
This project utilizes a dataset from Kaggle containing original fundus images and their corresponding ground truth annotations.
Natural language processing
Natural Language Processing, Course, 2023
This project covers the foundational concepts and essential techniques in natural language processing (NLP) using Python and Linux.
Chatbot
Natural Language Processing, NLP, 2023
This project involves developing a chatbot that delivers specific answers to a predefined set of questions.
Digital image processing, Dr. Hasanpour
Image Processing, Course, 2023
This project features the conversion of MATLAB code and algorithms from Dr. Hasanpour’s “Digital Image Processing” book into Python.
Digital image processing, Dr. Gonzalez
Image Processing, Course, 2023
This project involves converting MATLAB code and algorithms from Dr. Rafael C. Gonzalez’s renowned book “Digital Image Processing” into Python.
CIFAR-10 Classification Using Multi-Layer Perceptron and Regularization
Machine Learning, Automated Classification, 2022
This project showcases a classification model for the CIFAR-10 dataset, using a Multi-layer Perceptron (MLP) with regularization techniques to enhance performance and prevent overfitting.
MNIST classifier
Machine Learning, Automated Classification, 2022
This project implements a simple neural network for classifying MNIST digits using only one dense layer.