shapes like circle, star, square and rectangle are classified using support vector machine

🌻Flower Recognition using Multi-class classification 🌻

Real time Human Detection in webcam

このリポジトリは、顔検出および特徴量抽出のための様々な Python スクリプトを含んでいます。各スクリプトは異なるアルゴリズムや手法を使用しており、簡単に実行できるようになっています。

Satelite image classification in South Zone, Rio de Janeiro

Traffic Signs Detection And Recognision Using HOG and Deep Learning

This repository contains the code for Comparing Deep Learning and Classical Computer Vision for Semantic Segmentation: A comprehensive analysis of cutting-edge techniques and algorithms for precise object segmentation in computer vision tasks. This work was done under the Computer Vision course at IIT Jodhpur.

K-means clustering is an algorithm that groups similar data points into a predetermined number of clusters by minimizing the sum of squared distances between data points and their cluster centroids.

Detection algorithms and applications from famous papers; simple theory; solid code.

Image Processing with openCV4 & Python

This project focuses on implementing various image classification algorithms using machine learning techniques. The algorithms are trained on pixel values as well as feature extraction methods such as Histogram of Oriented Gradients (HOG), Scale-Invariant Feature Transform (SIFT), and Speeded-Up Robust Features (SURF). The classification is perform

Face recognition is used for to unlocking cell phones. And with recent advancements in deep learning,In this repository how to develop a face recognition system that can detect faces in images, identify the faces, and even modify faces with "digital makeup" like you've experienced in popular mobile apps

Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram

Computer Vision and Image Recognition algorithms for R users

Histogram of Oriented Gradients and Object Detection

ML project for two datasets House prices, and Fashion MSNIST

ROS package for pedestrian detection

Final Electronic Engineering project

Cat vs. Dog classification model using traditional ML methods, including data collection, splitting, HOG feature extraction, model training (e.g., SVM, Decision Tree), and fine-tuning via Grid Search.

ML classifier trained with raw pixels as features and high level features and then comparing their accuracies