CMU Computer Vision Projects

Implementing computer vision algorithms and applications

Scene Classification System using Bag-of-words Approach with Spatial Pyramid Extension

Feature Extraction based on Filter Banks
K Means Clustering
Visual Word Dictionary
Scene Classification

Augmented Reality with Planar Homographies

Direct Linear Transform
Matrix Decomposition to calculate Homography
Limitations of Planar Homography
FAST Detector and BRIEF Descriptors
Feature Matching
Compute Homography via RANSAC
Automated Homography Estimation and Warping
Augmented Reality Application using Homography

Lucas-Kanade Object Tracking

Simple Lucas & Kanade Tracker with Naive Template Update
Lucas & Kanade Tracker with Template Correction

Structure from Motion - 3D Reconstruction

Fundamental Matrix Estimation using Point Correspondence
Metric Reconstruction
Retrieval of Camera Matrices up to a Scale and Four-Fold Rotation Ambiguity
Triangulation using the Homogeneous Least Squares Solution
3D Visualization from a Stereo-Pair by Triangulation and 3D Locations Rendering

Neural Networks for Recognition

Manual Implementation of a Fully Connected Network
Training the Implemented Models
Text Extraction from Images of Handwritten Characters
Image Compression with Autoencoders

Photometric Stereo

Calibrated Photometric Stereo
Uncalibrated Photometric Stereo