Monday, April 7, 2008
Sunday, April 6, 2008
Automated Photo Tagging in Facebook
We looked into, whether this task could be performed by a computer. The presented automatic facial tagging system is split into three subsystems: obtaining image data from Facebook, detecting faces in the images and recognizing the faces to match faces to individuals. Firstly, image data is extracted from Facebook by interfacing with the Facebook API. Secondly, the Viola-Jones’ algorithm is used for locating and detecting faces in the obtained images. Furthermore an attempt to filter false positives within the face set is made using LLE and Isomap. Finally, facial recognition (using Fisherfaces and SVM) is performedon the resulting face set. This allows us to match faces to people, and therefore tag users on images in Facebook. The proposed system accomplishes a recognition accuracy of close to 40%, hence rendering such systems feasible for real world usage.
Download project report (with Harry Robertson, Hao Zou)
Cite as:
@ARTICLE{schuon_fb07,
title={Automated Photo Tagging in Facebook},
author={Schuon, Sebastian and Robertson, Harry and Zou, Hao},
journal={Stanford CS229 Fall 2007 Project Report},
year={2007}}
Saturday, April 5, 2008
Head Motion Controlled Break Out
One can think of several techniques to track the head of the user, but I found a very simple one to work fast (realtime and low cpu usage is a huge concern for a game) and reliable: simply thresholding the image for bright pixels and then averaging their position to a mean position, assumed to be the head. This position might by no means be really the head, since other bright objects might be in the view of the camera. But these are normally static, hence the only way to change the mean position is by moving the (illuminated) head. The amount the paddle is moved by a certain head movement might be different from background to background, but the human can adapt to that intuitively.
To see the technique in action, see this video:
Machine Learning: Locally Linear Embedding
Download Report (Bachelor Thesis)
HDR-Imaging for Welding
Original Scene |
|
The image on the left shows the scene captured by a standart camera. Here the scene comprimises both a bright light source and some text on the right. Using the suggested HDR technique, we can craft an image which contains both the detailed structure of filament of the light source and the text in readable form. Below you can see the four images channels of the raw bayer image:
For other details on the project, see the Visible Welding Homepage for details.
Motion Deblurring
For more details visit the separate Motion Deblurring Page.
Cite as:
@ARTICLE{schuon_deblur06,
title={Comparison of Motion Deblur Algorithms and Real World Deployment},
author={Schuon, Sebastian and Diepold, Klaus},
journal={57th International Astronautical Congress},
year={2006}}
MOKE: Images of Micromagnets
Whisker
The first video clip shows the process of establishing domain structures by exposing the target to a time-variant magnetic field. Here the target was a Whisker. The lumminance of the target corresponds directly to the magntic field strength. The images captured have been overlayed in software by vectors of the magnetic flux direction.
CU-Sheet
In this second clip the target was a cupper sheet. Contrary to the Whisker we observe a different domain pattern (a stacked one compared to the Landau-Lifshitz structure before). Furthermore defects in the target material lead to earlier domain forming.
The Project Report and a Poster are unfortunatly available in German only.
Intelligent Weather Station
A legacy thing, back from highschool. Since that was in Germany, the docs are also German...
Abstract:
"Als Ziel habe ich mir eine Wetterstation gesetzt, welche selbstständig arbeitet und sämtliche Messwerte digital erfasst. Alle Sensoren sollen mittels eine 1-Wire-Netzes untereinander vernetzt werden, damit ein Computer die Messwerte verarbeiten kann. Anhand dieser Werte soll die Software eigenständig Entscheidungen treffen und gegebenenfalls Warnmeldungen ausgeben.
Das Ergebnis dieser Überlegungen ist eine Wetterstation mit Feuchte-, Niederschlags-, Temperatur-, Luftdruck-, Windgeschwindigkeits-, Windrichtungs- und Sichtweitensensoren. In Arbeit befinden sich im Moment weitere Sensoren zur Erfassung des Grundwasserpegels, der Zustände der Feuermelder und der Dachflächenfenster. Eine Kombination der Sensordaten ermöglicht der Software Warnmeldungen an den Benutzer („Es regnet, bitte Fenster schließen“ oder „Vorsichtshalber Pumpen im Keller installieren, Grundwasserspiegel ist gefährlich hoch“). Diese Meldungen können entweder per Internet oder SMS zum Benutzer gelangen. Außerdem bietet die Software ein Web-Interface zur Auswertung aller Sensordaten. Ebenfalls können die Sensordaten in Standardformaten exportiert werden um z.B. Klimadiagramme zu erstellen."
Download Report (German only)