Last UpDate 13.11.2003 5:36 PM






  My dream is to measure the entire surface of a 3D moving object (in a certain volume)
and to reproduce it in a virtual environment in order have a real-time measurement of its
deformation and its position.




Research Interests:

  Signal, image/video processing and communication, computer vision, medical imaging;

  Multi-dimensional scanning, 3D measurement and other multimedia applications;

  Camera calibration methods and Computational Geometry;

  Computer graphics/animation and image based rendering;

  Medical Imaging & Biomedical Instrument Development;


Past Research:

  Subpixel measurement of target displacement

  Optical Flow

  Stereo disparity

  Image filtering


Current Research:

3D Scanning of moving objects based on "one snap" FTP methods;

Fully Automated cameras calibrations methods;

  Chessboard grid calibration based on a double Radon Transform.

Colour Space Transformation;


  3D Scanning of moving objects based on "one snap" FTP methods


My Ph D project consists in designing a 3D scanner to perform surface measurement of moving objects.
The 3D scanners are gaining huge spreading in the market today, but the great majority of them can only scan surfaces of stationary objects.
The innovation of the project consists in extracting the surface skin of an object without imposing it to stop.

The calibration grid

The scanner

You will find here following some preliminary results:



          The otput with the texture







Click on The image to enlarge it !


The Vrml file of the output is about 19MB
even zipped it was to big to link here
If You need It please e-mail me



 Chessboard grid calibration based on a double Radon Transform


     The goal of this work was:

  Minimize the time needed to calibrate a 3D FTP Scanner

  automatizing the Calibration Process

  Create a robust algorithm

  insensitive to the aberration caused by the lenses
  insensitive to generic backgrounds



When the scanner is calibrated?

Camera & Projectors

- Intrinsic parameters:

o Focal length
o Principal point
o Skew coefficient (angle between the x and y pixel axes )
o Distortions (radial and tangential)

- Extrinsic parameters:

o Rotations
o Translations




Innovation of the method


  It can extract corners even if the chessboard is partially occluded or if the corners are outside the image

  It can exclude all the corners which are not on the chessboard



The Double Radon Transform









The Klaus Kohlmann algorithm

  It consists in a 2D Hilbert transform

  Limit of the Algorithm
          applicable only in the range from - 5° to 5° rotations







Combining the two algorithms potentialities



  In this case DRT is fundamental to give a good estimate of the reference frame connected to chessboard grid







  Computing than a suitable prospective transform to obtain the edge of the grid be parallel to the axis image




  Than Klaus Kohlmann algorithm will give us the right estimation of the corners positions with subpixel Accuracy






Click on the image to open the movie