C

computer vision

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

An AI-driven pipeline for detecting and quantifying cancer and inflammatory tissue on biopsy slides to support standardized diagnostics. This independent fork focuses on advanced ensembling (SMP <-> nnU-Net), probability fusion, and Test-Time Augmentation (TTA), providing a highly reproducible training and inference workflow.

WASSfast is the next-generation stereo processing pipeline for sea waves 3D reconstruction.

This repo will have all resources, labs, data which I use/d on Kaggle Network

Sistema de detección automática de frutos maduros de café en imágenes reales usando filtros espaciales y morfología matemática. Incluye segmentación en HSV, limpieza de ruido y conteo preciso mediante contornos.

Detección automatizada de deforestación en la Amazonía usando segmentación por umbral, color y morfología. Incluye análisis temporal (2000–2019) del Bosque Nacional Jamanxim a partir de imágenes satelitales.

Projet de positionnement spatial par analyse visuelle mené dans le cadre du cours de Computer Vision à CentraleSupélec.

A real-time human counting system with age and gender classification.

Trabajo en que se demuestra el trabajo con imágenes satelitales

Marine biologists engaged in the study of coral reefs invest a significant portion of their time in manually processing data obtained from research dives. The objective of this challenge is to create an image segmentation pipeline that accelerates the analysis of such data. This endeavor aims to assist conservationists and researchers in enhancing their efforts to protect and comprehend these vital ocean ecosystems. Leveraging computer vision for the segmentation of coral reefs in benthic imagery holds the potential to quantify the long-term growth or decline of coral cover within marine protected areas.

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.

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 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.

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.

Playing around with OpenCV

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