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 detector from HOG + Linear SVM framework

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

The histogram of oriented gradients (HOG) implemented in tensorflow.

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

Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai

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.

Using Support Vector Machine and Extracting Histogram of Oriented Gradient Features

Histogram of oriented gradients (HOG) on GPU

In this repository, we implement common image processing techniques in Python and fully describe their algorithms.

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.

Um pouco dos trabalhos desenvolvidos durante a disciplina de Visão Computacional <3

Program for Harris Corner Detection with non-maximum Suppression, HOG Feature Extraction, Feature Comparison, Gaussian Noise and Smoothing.

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.