Image Recoloring using Gaussian Mixture Model and Expectation Maximization [OpenCV, w/Code]

Hi,
I'll present a quick and simple implementation of image recoloring, in fact more like color transfer between images, using OpenCV in C++ environment. The basis of the algorithm is learning the source color distribution with a GMM using EM, and then applying changes to the target color distribution. It's fairly easy to implement with OpenCV, as all the "tools" are built in.

I was inspired by Lior Shapira's work that was presented in Eurographics 09 about image appearance manipulation, and a work about recoloring for the colorblind by Huang et al presented at ICASSP 09. Both works deal with color manipulation using Gaussian Mixture Models.

Update 5/28/2015: Adrien contributed code that works with OpenCV v3! Thanks! https://gist.github.com/adriweb/815c1ac34a0929292db7

Let's see how it's done!
Continue reading "Image Recoloring using Gaussian Mixture Model and Expectation Maximization [OpenCV, w/Code]"

Share

Iterative Closest Point (ICP) for 2D curves with OpenCV [w/ code]

ICP - Iterative closest point, is a very trivial algorithm for matching object templates to noisy data. It's also super easy to program, so it's good material for a tutorial. The goal is to take a known set of points (usually defining a curve or object exterior) and register it, as good as possible, to a set of other points, usually a larger and noisy set in which we would like to find the object. The basic algorithm is described very briefly in wikipedia, but there are a ton of papers on the subject.

I'll take you through the steps of programming it with OpenCV.
Continue reading "Iterative Closest Point (ICP) for 2D curves with OpenCV [w/ code]"

Share

Bust out your own graphcut based image segmentation with OpenCV [w/ code]

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...
Continue reading "Bust out your own graphcut based image segmentation with OpenCV [w/ code]"

Share

Quick and Easy Head Pose Estimation with OpenCV [w/ code]


Update: check out my new post about this http://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

Continue reading "Quick and Easy Head Pose Estimation with OpenCV [w/ code]"

Share

Implementing PTAM: stereo, tracking and pose estimation for AR with OpenCV [w/ code]

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.
Continue reading "Implementing PTAM: stereo, tracking and pose estimation for AR with OpenCV [w/ code]"

Share