Annotate anything
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Updated
Jun 9, 2023 - Python
Annotate anything
The repository provides code for automatic segmentation using YOLOv5 and Segment Anything Model (SAM), which is used to create stickers.
Wi-Fi6 Collision avoidance simulator for Microsoft AirSim
A repository containing implementations of famous Vision Architectures over the years
PIRDS - Prescription Image Recognition and Digitalizing System is a OCR make with Tensorflow that digitalises images of Prescription of Handwritten Texts by Doctors.
This repository refers to the article Semantic Image Collection Summarization with Frequent Subgraph Mining.
Project that aims to track each player and tell for which team they play. This project was developed and tested only in gymnasium sports such as futsal, basketball and volleyball
1D CNN-based segmentation of hyperspectral satellite images from the NTNU SmallSat Lab HYPSO-2 mission.
wai.annotations plugin for making predictions via models communication through Redis backend.
A tool for the automatic segmentation of fetal brain 3D T2-weighted MRI
A computer vision-based project that can estimate the measurement of the entire body from an image. Utilized the technologies involving Python, OpenCV, and Mediapipe. Used the techniques including image segmentation, contour detection and pixel per metric ratio
A Unet implementation from original paper
Códigos relacionados ao artigo preparado para o evento WCAMA 2021.
A lite image segmentation model to calculate volume of char in a container
An Image Segmenting based power adjusting system for a Ring shooter system designed for RoboCon 2023
Image processing to deep learning practice codes.
DriveSafe-PotholeDetect uses AI to detect potholes in real-time, improving road safety. This project demonstrates how technology can prevent accidents and enhance driving experiences.
A skin detection system using image processing and machine learning to classify pixels as skin or non-skin. Implements Python, OpenCV, and algorithms like Logistic Regression and Random Forest for segmentation and classification.
Pixelwise classification of wounds in laboratory rat images: A comparison between the threshold methods, Random Forest algorithm and the UNet deep model
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