This is for my Computer Vision quiz practice, we only taught to match 2 images, though. So, I challenge myself to create an image matching program to detect more than 2 images from 2 different directories (the object & scene) using SURF & FLANN.

Testing various detector / descriptor combinations to see which ones perform best to be used in a collision detection system. Also 2 different approaches (FLANN vs. Brute-force with the descriptor distance ratio test) for keypoints matching are tested.

Codes written during course Nature Inspired Computing

Example Image Recognition using OpenCV 3.0 (custom filters, flann & surf)

📽️🖥️ Track objects on your camera or on a video.

Tracking the preceding vehicle using Lidar and camera sensors to calculate the Time To Collision (TTC).

Here you can find some commonly used algorithms in 3D image processing (3D Bildverarbeitung).

Research NN searching method FLANN

Utilized OpenCV, ORBDescriptors, FLANN, Homography/Affine Transformations, and a multi-layer convolutional architecture to do direct image matching via feature and key-point matching for scale-variant images

数字媒体作业

Panorama Image Stitching Using SIFT and SURF Keypoint Descriptors

Clustering scenes

Python bindings for FLANN - Fast Library for Approximate Nearest Neighbors. (With Python 3 compatibility patches)

A package for registration of wide range astronomical images of stars, galaxies and clusters using SIFT, FLANN and RANSAC algorithms for extreme precision applications

Project: 2D Feature Tracking || Udacity: Sensor Fusion Engineer Nanodegree

6 DOF arm manipulation, utilizing knowledge-based reasoning, BDI and HTN planning

A vision-based nutrition management system (mobile + server) for users, where users can track the calorie and nutrient intake of the food products purchased, using the concepts of image processing, computer vision and machine learning to accurately predict names of cereal boxes, fruits, vegetables etc. The images are classified using the concept…

Processing videos of digital electricity meter to extract readings using OpenCV and Python

Object 2D Pose Estimation trained by RGB Data - Using Intel® RealSense D435