This repository allows you to anonymize sensitive information in images/videos. The solution is fully compatible with the DL-based training/inference solutions that we already published/will publish for Object Detection and Semantic Segmentation.

A novel local intensity order transformation for robust curvilinear object segmentation(TIP)

Repositório dos exemplos e desafios utilizados na disciplina de Visão Computacional do curso de MBA Machine Learning da FIAP

Unsupervised Image Transformation Learning via Generative Adversarial Networks

An MFC-based digital image processing program: 这是一个基于MFC的数字图像处理程序，主要实现了对图像进行灰度变换（曲线、色阶）、几何变换（缩放、旋转）、滤波（空间滤波、频域滤波、FFT）等功能。

Circular Hough transform is used for detecting circles in images. This is a basic implementation of CHT using Matlab

Transforms the perspective of an image and draw it on a canvas

Implementation of the paper "Style Transfer for Keypoint Matching under Adverse Condition"

library to modify images and the corresponding landmarks

Webapp for optimizing projector guided painting.

Image Transformer to ASCII and PixelArt made by me using P5.js

One-class classification approach using error of image transformation into one image

Google Cloud Platform App Engine application in Python 3.7 runtime for text detection with Vision API.

Simple samples to demonstrate 2D Thin Plate Spline Warping.

Video thumbnails using Kaltura's Image Transformation API and JavaScript. Including low-rest blurred loading while animation stripe loads in the backgroud.

The Flutter App transforming Human's face into Chimpanzee-like face.

Python script that creates an image based on minecraft blocks.

This project showcases a variety of digital image processing techniques implemented in MATLAB. It includes graphical user interfaces (GUIs) for tasks such as bit gray slicing, filtering, histogram equalization, and more. Designed for both educational and practical applications, it aims to enhance understanding of image processing concepts.

This repo contains implementations of some of the classical computer vision algorithms/techniques for feature extraction, feature matching, image transformation, color image reconstruction, image denoising, image classification, and image segmentation.