This is a tutorial on using Graph-Cuts and Gaussian-Mixture-Models for image segmentation with OpenCV in C++ environment.
Update 10/30/2017: See a new implementation of this method using OpenCV-Python, PyMaxflow, SLIC superpixels, Delaunay and other tricks.
Been wokring on my masters thesis for a while now, and the path of my work came across image segmentation. Naturally I became interested in Max-Flow Graph Cuts algorithms, being the “hottest fish in the fish-market” right now if the fish market was the image segmentation scene.
So I went looking for a CPP implementation of graphcut, only to find out that OpenCV already implemented it in v2.0 as part of their GrabCut impl. But I wanted to explore a bit, so I found this implementation by Olga Vexler, which is build upon Kolmogorov’s framework for max-flow algorithms. I was also inspired by Shai Bagon’s usage example of this implementation for Matlab.
Let’s jump in…
Category: code
Update: check out my new post about this https://www.morethantechnical.com/2012/10/17/head-pose-estimation-with-opencv-opengl-revisited-w-code/
Hi
Just wanted to share a small thing I did with OpenCV – Head Pose Estimation (sometimes known as Gaze Direction Estimation). Many people try to achieve this and there are a ton of papers covering it, including a recent overview of almost all known methods.
I implemented a very quick & dirty solution based on OpenCV’s internal methods that produced surprising results (I expected it to fail), so I decided to share. It is based on 3D-2D point correspondence and then fitting of the points to the 3D model. OpenCV provides a magical method – solvePnP – that does this, given some calibration parameters that I completely disregarded.
Here’s how it’s done
Hi
Been working hard at a project for school the past month, implementing one of the more interesting works I’ve seen in the AR arena: Parallel Tracking and Mapping (PTAM) [PDF]. This is a work by George Klein [homepage] and David Murray from Oxford university, presented in ISMAR 2007.
When I first saw it on youtube [link] I immediately saw the immense potential – mobile markerless augmented reality. I thought I should get to know this work a bit more closely, so I chose to implement it as a part of advanced computer vision course, given by Dr. Lior Wolf [link] at TAU.
The work is very extensive, and clearly is a result of deep research in the field, so I set to achieve a few selected features: Stereo initialization, Tracking, and small map upkeeping. I chose not to implement relocalization and full map handling.
This post is kind of a tutorial for 3D reconstruction with OpenCV 2.0. I will show practical use of the functions in cvtriangulation.cpp, which are not documented and in fact incomplete. Furthermore I’ll show how to easily combine OpenCV and OpenGL for 3D augmentations, a thing which is only briefly described in the docs or online.
Here are the step I took and things I learned in the process of implementing the work.
Update: A nice patch by yazor fixes the video mismatching – thanks! and also a nice application by Zentium called “iKat” is doing some kick-ass mobile markerless augmented reality.
Hi
In the past few weeks I have been working hard at a few projects for end-of-term at Uni. One of the projects is what I called “SmartHome”, for Embedded computing [link] course, is a home monitoring [link] application. In the course the students were given an LPC2148 arm7-MCU (NXP) based education board, implemented by Embedded Artists [link]. My partner Gil and I decided to work with ZigBee extension modules [link] to enable remote communication.
Here are the steps we took to bring this project to life.
Hi
I wanted to do the simplest recoloring/color-transfer I could find – and the internet is just a bust. Nothing free, good and usable available online… So I implemented the simplest color transfer algorithm in the wolrd – Histogram Matching.
Here’s the implementation with OpenCV