amal-vincent Goto Github PK

adience_align

This project provides alignment tools for faces, to be used as a preprocessing step before computer vision tasks on face images

amal-vincent.github.io

Personal website

awesome-computer-vision

A curated list of awesome computer vision resources

awesome-deep-learning-papers

The most cited deep learning papers

awesome-deep-vision

A curated list of deep learning resources for computer vision

caffe-gui-tool

Node based Gui for creating caffe networks

code

Code for the book "Mastering OpenCV with Practical Computer Vision Projects" by Packt Publishing 2012.

computer-vision-resources

dashboard

Dashboard for SWAPP

deepgaze

Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking.

dive-into-machine-learning

Dive into Machine Learning with Python Jupyter notebook and scikit-learn

dlib

A toolkit for making real world machine learning and data analysis applications in C++

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

eyetab

facex

A high performance open source face landmarks detector, based on explicit shape regression algorithm.

gaze-estimation

This repository includes the program for gaze estimation using ordinary webcam, for Human Computer interface based on our research paper and the modules used for the design and analysis of the system

gestocon

This is the basic gesture set implementation for touch based social games

imgaug

Image augmentation for machine learning experiments.

machine-learning-for-software-engineers

A complete daily plan for studying to become a machine learning engineer.

monash-job-scraper

This script scrapes the monash career gateway website and builds a database of all new job listings. An email is then send to the specified user when ever a new job has been posted. Please see the readme to see how to use the script

opencv

Open Source Computer Vision Library

opencv-python-blueprints

OpenCV with Python Blueprints: Design and develop advanced computer vision projects using OpenCV with Python

papers

Summaries of machine learning papers

pattern_classification

A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks

pycv-time

A collection of computer vision code pieces / tutorials and other useful stuff

pyvision

Official github branch from the source forge site.

simplecv

The Open Source Framework for Machine Vision

synthetic-computer-vision

A list of synthetic dataset and tools for computer vision

t-cnn

ImageNet 2015 Object Detection from Video (VID)

tensorflow-examples

TensorFlow Tutorial and Examples for beginners