superviseddescent - C++11 implementation of the supervised descent optimisation method

  •        33

superviseddescent is a C++11 implementation of the supervised descent method, which is a generic algorithm to perform optimisation of arbitrary functions. The library contains an implementation of the Robust Cascaded Regression facial landmark detection and features a pre-trained detection model.

http://patrikhuber.github.io/superviseddescent/
https://github.com/patrikhuber/superviseddescent

Tags
Implementation
License
Platform

   




Related Projects

4dface - Real-time 3D face tracking and reconstruction from 2D video

  •    C++

This is a demo app showing face tracking and 3D Morphable Model fitting on live webcams and videos. It builds upon the 3D face model library eos and the landmark detection and optimisation library superviseddescent. Clone with submodules: git clone --recursive git://github.com/patrikhuber/4dface.git, or, if you've already cloned it, get the submodules with git submodule update --init --recursive inside the 4dface directory.

sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)

  •    C

SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

OpenFace - OpenFace – a state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation

  •    C++

Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace – a tool intended for computer vision and machine learning researchers, affective computing community and people interested in building interactive applications based on facial behavior analysis. OpenFace is the first toolkit capable of facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation with available source code for both running and training the models. The computer vision algorithms which represent the core of OpenFace demonstrate state-of-the-art results in all of the above mentioned tasks. Furthermore, our tool is capable of real-time performance and is able to run from a simple webcam without any specialist hardware. OpenFace is an implementation of a number of research papers from the Multicomp group, Language Technologies Institute at the Carnegie Mellon University and Rainbow Group, Computer Laboratory, University of Cambridge. The founder of the project and main developer is Tadas Baltrušaitis.

ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

  •    Python

A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

  •    CSharp

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.


raster-vision - deep learning for aerial/satellite imagery

  •    Python

Note: this project is under development and may be difficult to use at the moment. The overall goal of Raster Vision is to make it easy to train and run deep learning models over aerial and satellite imagery. At the moment, it includes functionality for making training data, training models, making predictions, and evaluating models for the task of object detection implemented via the Tensorflow Object Detection API. It also supports running experimental workflows using AWS Batch. The library is designed to be easy to extend to new data sources, machine learning tasks, and machine learning implementation.

luminoth - Deep Learning toolkit for Computer Vision

  •    Python

Luminoth is an open source toolkit for computer vision. Currently, we support object detection, but we are aiming for much more. It is built in Python, using TensorFlow and Sonnet. Read the full documentation here.

eos - A lightweight 3D Morphable Face Model fitting library in modern C++11/14

  •    C++

eos is a lightweight 3D Morphable Face Model fitting library that provides basic functionality to use face models, as well as camera and shape fitting functionality. It's written in modern C++11/14. An experimental model viewer to visualise 3D Morphable Models and blendshapes is available here.

openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation

  •    C++

OpenPose represents the first real-time multi-person system to jointly detect human body, hand, and facial keypoints (in total 135 keypoints) on single images. For further details, check all released features and release notes.

CatPapers - Cool vision, learning, and graphics papers on Cats!

  •    HTML

As reported by Cisco, 90% of net traffic will be visual, and indeed, most of the visual data are cat photos and videos. Thus, understanding, modeling and synthesizing our feline friends becomes a more and more important research problem these days, especially for our cat lovers. Cat Paper Collection is an academic paper collection that includes computer graphics, computer vision, machine learning and human-computer interaction papers that produce experimental results related to cats. If you want to add/remove a paper, please send an email to Jun-Yan Zhu (junyanz at berkeley dot edu).

opencv4nodejs - Asynchronous OpenCV 3

  •    C++

By its nature, JavaScript lacks the performance to implement Computer Vision tasks efficiently. Therefore this package brings the performance of the native OpenCV library to your Node.js application. This project targets OpenCV 3 and provides an asynchronous as well as an synchronous API. The ultimate goal of this project is to provide a comprehensive collection of Node.js bindings to the API of OpenCV and the OpenCV-contrib modules. An overview of available bindings can be found in the API Documentation. Furthermore, contribution is highly appreciated. If you want to get involved you can have a look at the contribution guide.

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

  •    C++

Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference. Doing so will make some things run faster.

pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, speaker embedding

  •    Python

Open Phd/postdoc positions at LIMSI combining machine learning, NLP, speech processing, and computer vision. If you use pyannote.audio in your research, please use the following citations.

cvat - Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

  •    Javascript

CVAT is completely re-designed and re-implemented version of Video Annotation Tool from Irvine, California tool. It is free, online, interactive video and image annotation tool for computer vision. It is being used by our team to annotate million of objects with different properties. Many UI and UX decisions are based on feedbacks from professional data annotation team. Code released under the MIT License.

PyVision Computer Vision Toolkit

  •    Python

PyVision is a object-oriented Computer Vision Toolkit for researchers that contains vision and machine learning algorithms and algorithm analysis and easily interfaces with scipy/numpy, PIL, opencv and other computer and machine learning libraries.

Computer-Vision-Basics-with-Python-Keras-and-OpenCV - Full tutorial of computer vision and machine learning basics with OpenCV and Keras in Python

  •    Jupyter

This was created as part of an educational for the Western Founders Network computer vision and machine learning educational session. Note: Please check the issues on this repo if you're having problems with the notebook.

VisionFaceDetection - An example of use a Vision framework for face landmarks detection in iOS 11

  •    Swift

First one is face rectangle detection by using VNDetectFaceRectanglesRequest based on pixelBuffer provided by delegate function captureOutput. Next we need to setup the property inputFaceObservations of VNDetectFaceLandmarksRequest object, to provide the input. Now we are redy to start landmarks detection.