Pipeline to convert real-life chess boards into a 2D digital format(FEN) from images and live camera feeds. The system has 2 versions: one for real-time processing using the OAK-D Lite camera and another for high-precision analysis of static images. YOLO is used to detect chess pieces, and traditional cv methods determine board positioning

Computer vision projects focused on object detection, object tracking, classical computer vision techniques, image segmentation, feature extraction algorithms, and more. Python and C++ implementations available.

This repo contains implementations of some of the classical computer vision algorithms/techniques for feature extraction, feature matching, image transformation, color image reconstruction, image denoising, image classification, and image segmentation.

Python implementation of Laplacian pyramid algorithm for blending images using reduce/expand, Gaussian/Laplacian pyramids, and combine/collapse functions for realistic outputs

Lane Detection using classical approach and a deep learning based approach

Implementation of Classical Structure from Motion pipeline

Multi-object tracking of water fleas from video. Detects dark blotches on light background, performs multi-object association, tracks them with Kalman filters.

A multi-stage computer vision algorithm to detect edges in images. Developed by John Canny in 1986, widely used in image processing, computer vision, and other fields. This repo provides an implementation in Python

just a redo of a previously done project using python 3.13 ;)

Alignment of an unaligned image with a base/reference image using feature detection, feature matching, and homography in OpenCV.

This project involves developing a simplified boundary detection algorithm that combines texture, brightness, and color gradients with classical edge detection methods like Sobel and Canny. The final boundary map is generated by fusing these feature gradients with traditional edge detection methods for more robust and accurate edge detection.

A collection of computer vision algorithm implementations, covering various techniques and transformations.

Haar cascade classifiers detecting faces and eyes in images with OpenCV.

just a classical approach to the green screen effect

Environment to explore AI/DL algorithms, with experiments and results. | Explored: Docker; CI/CD; Foundation Models (V+L); Chat LLMs+RAG; Sensor Fusion; Computer Vision; 2D/3D data generation

This project captures frames from a webcam and stitches them together into a panorama using OpenCV's and optionally saves the final panorama.

Computer Vision Course (CS-GY 6643) - Lab Assignments + Projects

Real-time OpenCV face & blur detection system with Flask-based live video streaming. Focused on classical CV and production-aware design.

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.

Image compositing and alpha matting in Python with OpenCV and NumPy; artifact color correction, matte generation, and foreground-background blending. 🐙