There are 1 repository under histogram-of-oriented-gradients topic.
Vehicle detection, tracking and counting by SVM is trained with HOG features using OpenCV on c++.
The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.
Object detection program, using HoG+SVM, OpenCV2.4.10, VS2013
Bag of Visual Words and HOG based object detection in python/opencv
Detect traffic sign and recognize them using Image Processing algorithms and Machine Learning(Random Forest)
This algorithm counts occurrences of gradient orientation in localized portions of an image and visualize it in an image.
Fast computation of rectangular histogram of oriented gradients (R-HOG) features using integral histogram
Python implementation of 3D Voxel HOG from the paper "A 3D Scene Analysis Framework and Descriptors for Risk Evaluation" by Rob Dupre, Vasileios Argyriou, D. Greenhill, Georgios Tzimiropoulos.
Worked on a Medical Computer Vision project involving Parkinson's Disease Detection by using Histogram of Oriented Gradients (HOG), Machine Learning and OpenCV on the images generated by the Spiral-Wave test
Histogram of oriented gradients (HOG) on GPU
Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai
Using Support Vector Machine and Extracting Histogram of Oriented Gradient Features
Program for Harris Corner Detection with non-maximum Suppression, HOG Feature Extraction, Feature Comparison, Gaussian Noise and Smoothing.
This repository hosts a project that aims to tackle the issue of plagiarism in handwritten submissions. The system developed based on Multi-level detection using the combination of digital pattern analysis and text content comparison to detect potential instances of plagiarism.
In this repository, we implement common image processing techniques in Python and fully describe their algorithms.
Um pouco dos trabalhos desenvolvidos durante a disciplina de VisĂŁo Computacional <3
Image Search via Feature Vectors.
Vehicle Detection and Tracking
Face Recognition with HOG (Histogram of Gradients) and LDB (Local Difference Binary) features using SVM Classifier
Use a Histogram of Oriented Gradients (HOG), Spatial Binning of Color, Histograms of Color, a Linear Support Vector Machine and multi-scale sliding windows for vehicle detection and tracking
Thermograms Classification To Breast Cancer Detection Using Statistical And Fractal Texture Features Extraction
Sketch based face recognition using AR and CUHK datasets. CNN and HoG methods are compared. Different augmentation techniques are applied due to limited data size.
A baseline/jump-off project for segmentation of cardiac regions and detection of myocardial infarction. Dataset used: https://www.kaggle.com/datasets/aysendegerli/hmcqu-dataset
MNIST dataset with several classifiers applied.
Train a SVM and for detecting human upper bodies in TV series The Big Bang Theory
This repo includes complete end to end algorithm for dog breed classification mechanism using deep learning.
The goal of this project is to solve the task of name transcription from handwriting images implementing a NN approach.
The purpose of this Attendance System Using Face System is to record the presence or attendance of employee through a browser by recognizing the faces captured through a webcam. For this record-keeping, a database was built to store the in-time and out-time of the employee.
Vehicle Detection and Tracking pipeline with OpenCV, HOG and SVM.
The goal is to create a pipeline to identify and track vehicles in a video from a front-facing camera on a car with a traditional Computer Vision approach for object detection: processing stages, feature extraction, spatial sampling and classification
Goal is to write a software pipeline to detect vehicles in a video using Support Vector Machines
Final Year Project: Predicting Charpy impact test data from microstructure data using a machine learning model.
Hybrid Histogram Oriented Gradient and Local Binary Pattern for Image Feature Extraction