🏄‍♂️ Surf GitHub code with VS Code. (with CLI, chrome extensions, also works for Gitlab)

Computer Vision and Image Recognition algorithms for R users

🌊 Aggregated surf forecast from Surfline, MagicSeaweed & Spitcast APIs

An open-source SwiftUI Stack Overflow client

Gear for safe Surf in Flutter

Machine Vision Toolbox for MATLAB

SURF - Speeded Up Robust Features - source code

Easy and powerful PHP deployment tool

Webapplication for image stitching and aligning

A simple script that handles bookmarks stored in a txt file.

My works for EE 569 - Digital Image Processing - Spring 2018 - Graduate Coursework at USC - Dr. C.-C. Jay Kuo

A CS:GO Surftimer for the z4lab Community

FST (Fast Succinct Trie) implementation in Rust

Manage sticky suckless surf windows.

Repositório utilizado para armazenar algoritmos de reconhecimento facial

本科毕业设计：针对Deepfake假脸视频面部细节特征的提取算法

Python ML programm to classify food using Deep Learning and feature classifiers like SIFT, SURF with Bag of Words and SVMs

personalized alert service for surf forecasts

Image Registration

Traffic sign detection and classification

Turnbinds (+left/+right) with customizable yawspeed for CS:GO

Match a cropped image to the original image with an efficient algorithm using Python and OpenCV.

Feature Detection and Description with SIFT, SURF, KAZE, BRIEF, ORB, BRISK, AKAZE and FREAK using Python and OpenCV

🏄 Global Game Jam 2017

Genetic algorithm in SourceMod for movement gamemodes

The objective of this work is to detect the cell phone and/or camera used by a person in restricted areas. The paper is based on intensive image processing techniques, such as, features extraction and image classification. The dataset of images is generated with cell phone camera including positive (with cell phone) and negative (without cell phone) images. We then extract relevant features by using classical features extraction techniques including Histogram of Oriented Gradients (HOG) and Speeded up Robust Features (SURF).The extracted features are then, passed to classifier for detection. We employ Support Vector Machine (SVM), Nearest Neighbor (K-NN) and Decision tree classifier which are already trained on our dataset of training images of persons using mobile or otherwise. Finally, the detection performance in terms of error rate is compared for various combinations of feature extraction and classification techniques. Our results show that SURF with SVM classifier gives the best accuracy.