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Anne - Face recognition using computer vision in IoT enviroment - 5th semester project developed at Paulista University

A Python project about facial recognition using various methodologies involves the development of a system that can detect and recognize human faces by applying a range of techniques and algorithms.

A Python project using deep learning for image recognition develops a system that applies convolutional neural networks (CNNs) to identify and classify objects, such as cars , enabling accurate recognition of various figures from training datasets.

Documentação do Trabalho

OpenCV implementation to display the histogram of 16-bits depth maps

Projeto de TCC baseado em processamento de imagens e visão computacional para identificar a evolução de ogras da engenharia civil

Computer Vision with Python and YOLO

Image classification and object recognition using the CNN DenseNet and EfficientNet convolutional neural networks with the Frozen Model and the Coffe framework. Subsequently, using the CNN MobileNet-SSD and YOLO convolutional neural networks with the TensorFlow framework.

Jogo da soma utilizando o OpenCV

The visual perception of a billboard, which suffers from lighting and noise issues, needs to be improved. Additionally, the billboard must be viewed frontally, which means the visual perspective must be changed from its current oblique position.

A crime scene reconstruction is planned by stitching several photographs together to reproduce the events. The photographs were taken from different perspectives to capture the greatest amount of visual information from the scene.

Development and implementation of a leukocyte detection and counting algorithm for diagnosing malaria using the thick blood smear technique (thick blood smear imaging). The algorithm should generalize the detection and counting process to all images.

Este repositorio contiene la solución al examen práctico de la asignatura de Visión por Computadora. Se abordan técnicas fundamentales de procesamiento de imágenes para la detección de regiones conectadas, identificación de grietas y localización de marcadores de referencia.