Available Student and Research Projects

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===To Do===
 
===To Do===
* Improve the correspondences using 2D cross-correlation (roll-angle will assumed to be low).
 
* (Re)implement the [http://panotools.sf.net/ panotools]-solver using [http://www.boost.org/libs/numeric/ublas/doc/index.htm boost::ublas] and [http://vision.eng.shu.ac.uk/jan/mimas/docs/group__lapack.html Mimas-lapack]
 
** Use correspondences to optimize the parameters (rotations, common focal length, rotation of virtual camera).
 
** Transform and merge the images into the resulting '''panorama image'''.
 
 
<math>
 
<math>
 
\lambda\,\begin{pmatrix}m^\prime_{1}\\m^\prime_{2}\\f\end{pmatrix}=
 
\lambda\,\begin{pmatrix}m^\prime_{1}\\m^\prime_{2}\\f\end{pmatrix}=
Line 66: Line 62:
 
\begin{pmatrix}m_{1}\\m_{2}\\f\end{pmatrix}
 
\begin{pmatrix}m_{1}\\m_{2}\\f\end{pmatrix}
 
</math>
 
</math>
* Transform and map the images into the resulting '''panorama image'''.
+
 
 +
* Improve the correspondences using 2D cross-correlation (roll-angle will assumed to be low).
 +
* (Re)implement the [http://panotools.sf.net/ panotools]-solver using [http://www.boost.org/libs/numeric/ublas/doc/index.htm boost::ublas] and [http://vision.eng.shu.ac.uk/jan/mimas/docs/group__lapack.html Mimas-lapack]
 +
** Use correspondences to optimize the parameters (rotations, common focal length, rotation of virtual camera).
 
** Adopt existing method for removing fringes (transitions/blending)
 
** Adopt existing method for removing fringes (transitions/blending)
 
Optional:
 
Optional:
 
* Automatically find correspondences
 
* Automatically find correspondences
  
See [[Panorama Stitching|page on panorama-stitching]] for more details.
+
Also see [[Panorama Viewer|panorama viewer]].
  
 
Contact [[User:Engjw|Jan Wedekind]] for a project in this area.
 
Contact [[User:Engjw|Jan Wedekind]] for a project in this area.

Revision as of 18:17, 20 April 2007

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Contents

Student Projects

We always offer projects to motivated students (first degree, Masters of Science, ERASMUS student, ...). If you are interested in doing a project in computer vision, let us know. Here is a description of ongoing research areas to give you an idea of possible projects. You can also suggest a research topic yourself.

If you work with us, you can learn a lot of skills which are relevant for a career as a software developer:

  • Computer Vision, Signal processing, Robotics
  • Linear Algebra, Analysis
  • Software Engineering

We are using state-of-the-art platform-independent software tools:

  • Source-code documentation with Doxygen logo.png doxygen
  • Cross-platform user-interfaces with Qt logo.png Qt. You can develop full-featured GUI-software which runs under Tux.jpg GNU/Linux, Ms-windows logo.png Microsoft Windows, and Macos.gif MacOS!
  • Platform-independent Stl logo.gif Standard Template Library
  • Platform-independent C--boost logo.gif Boost Library

Project areas

Stitching for microscopes

A bird's feather (reflected light, darkfield) (7.2 MByte video, 10.1 MByte video)
Stitching using the feedback of the microscope's drive

Premise

  1. A microscope-video of an object being moved in x-, and y-direction (parallel to the focussed plane)
  2. Later a microscope-video of an object being moved in x-, y-, and z-direction (i.e. including depth changes)

To Do

  • Generate stitched image from the input-video (linear complexity desirable) without feedback from microscope-drive
  • Cross-compare images to avoid a drift of the estimated shift
  • Later provide extended depth of field by maximising a focus measure.

See Also

External Links

Contact Jan Wedekind for a project in this area.

Automated photo stitching

Hilbre Island input images
Hilbreisland1.jpg Hilbreisland2.jpg Hilbreisland3.jpg Hilbreisland4.jpg Hilbreisland5.jpg Hilbreisland6.jpg Hilbreisland7.jpg
Resulting panorama image created with Hugin
Hilbreislandpanorama.jpg

Premise

  • A set of images taken with the same camera-settings (aperture, exposure time, focal length) and center of projection but different viewing directions (mainly yaw, pitch)
  • Manually selected correspondences.

To Do

<math> \lambda\,\begin{pmatrix}m^\prime_{1}\\m^\prime_{2}\\f\end{pmatrix}= \begin{pmatrix}h_{11}&h_{12}&h_{13}\\h_{21}&h_{22}&h_{23}\\h_{31}&h_{32}&h_{33}\end{pmatrix}\, \begin{pmatrix}m_{1}\\m_{2}\\f\end{pmatrix} </math>

  • Improve the correspondences using 2D cross-correlation (roll-angle will assumed to be low).
  • (Re)implement the panotools-solver using boost::ublas and Mimas-lapack
    • Use correspondences to optimize the parameters (rotations, common focal length, rotation of virtual camera).
    • Adopt existing method for removing fringes (transitions/blending)

Optional:

  • Automatically find correspondences

Also see panorama viewer.

Contact Jan Wedekind for a project in this area.

External Links

Physics Engine

Contact Jan Wedekind for a project in this area.

Premise

A physics engines is useful for simulating robots and testing computer vision algorithms. The Open Dynamics Engine was used in two projects already.

To Do

The Open Dynamics Engine is not numerically stable. An investigation into numerical algorithms for simulating multiple rigid bodies is required. The rigid bodies can be connected by joints, which are limiting their degrees of freedom.

See Also

External Links

RANSAC

Recognition and tracking with three or four degrees-of-freedom. More ...

Random sample consensus is a method for object recognition. This project is about recognising macroscopic rigid objects (e.g. household and office articles like cups, stapler, ...

To Do

  • Select point-features and a suitable similarity measure
  • Implement RANSAC algorithm and apply to 3 and 4 degrees-of-freedom problem.
  • Extend RANSAC implementation to 6 degrees-of-freedom problem (maybe use line- and point-features)
  • Demonstrate algorithm on real object.

See Also

See Also

External Links

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