C

computer vision

API4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes General Image Classification / Labelling API provided by API4AI.

API4AI is cloud-native computer vision & AI platform for startups, enterprises and individual developers. This repository contains sample mini apps that utilizes Fashion Apparel Recognition API provided by API4AI.

This is an additional implementation for a robot framework for the SLAM using camera, pose estimation and image segmentation of human. This is used with path planning techniques that allow human and robot to cohabitate in shared environment.

Códigos desenvolvidos ao longo das disciplinas que cursei

Train YOLOv5 for breed classification on Oxford Pets III dataset from scratch on Google Colab, and serve through Dockerized implementation of a flask-based HTML/JS frontend and an asynchronous API service on FastAPI. Use of MLFlow for logging.

This is the repo used to build the documentation about Sara Computer Vision Libraries (https://gitlab.com/oddkiva/sara)

The repository with supplementary code, data and an R package for article "Fast automatic deforestation detectors and their extensions for other spatial objects".

Image, Deep Learning, Ecology : please visit https://ecostat.gitlab.io/imaginecology/

A curated list of deep learning resources for computer vision in the context of ecology

This repository contains all my work related to the study of effectiveness of wavelet feature extraction on: Pose estimation Human segmentation Object detection Image Processing

Bioinformatics - Segmentation of biofilm images using OpenCV library.

Cpp console application to segment an image using computer vision techniques. I used many features in OpenCV library like -connectedComponentsWithStats to create the desired mask and -createTrackbar to adjust the parameters

Prediction of age from X-Ray images of hand bones using deep learning models. We used 3 models: Shallow, ResNet50, and InceptionV4. The best result achieved was with a mean absolute error of 10 months using InceptionV4. The preprocessing of data included computer vision techniques like CLAHE filter and reducing channels, and also creativities such as using the Google MediaPipe library to detect hands and crop on them.

Federated learning project. Using python , pytorch , Flower , Mlflow , Docker Create architecture to create , deploy model with Flower( binary classification) .

This project develops video analytics towards anomaly detection on edge for smart cities under low light conditions: https://iotgarage.net/projects/VideoAnalyticstowardsAnomalyDetectionontheEdge.html

Playing around with OpenCV

Private git for rendering ".ipynb" notebook files...

An application to support autistic children in emotion recognition.

dead by daylight skillcheck parameters analyzing software