DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE               
                    UNIVERSITY OF SOUTHERN DENMARK, ODENSE                   

                      Computer Vision to Animate Vision                      

                        Jens Arnspang and Jon Sporring                       
                                     DIKU                                    
                           University of Copenhagen                          

                    Tuesday, February 13, 2001, at 2:15 PM                   
                               The Seminar Room                              


The disciplines of Biological  Vision and Physical Optics  have been studied
as a  subject of  their own  right and  have  been an  inspiration to  early
research in Computer Vision.  Over the decades  the latter field has however
matured into a somewhat closed  study of the use of  pinhole models and flat
CCD chips.  The  result is  a  largely stagnating  field  with a  number  of
inherent and  severe  problems.  This  colloquium  will  put  forward  three
suggestions of a  careful revisit to  the origin  of image formation  and of
modelling of biological vision  in order to  create mathematical-algorithmic
vision paradigms that overcome such problems.

Firstly, the virtues  of ancient 3D  integral photography are  combined with
new 3D sensors, and it is shortly  hinted, that such arrangements might very
well overcome quite a  number of classic  ambiguity and scaling  problems in
computer vision.

Secondly, biophysics experimentation tells  that a revised  concept of shape
descriptors is a close conclusion to draw in  order to mimic 3D human visual
perception capabilities of objects in computer vision.  Classic differential
geometry  needs  to  be  supplemented  with  these  or  eventual  completely
abandoned for the purpose.

Thirdly, the concept of scale space  seems a fruitful mathematical modelling
concept in  several aspects  of algorithmic  vision.  The  issue of  general
Scale-Selection is addressed.  At least three views of image structure exist
in the image  processing community:  Mathematical morphology,  in which  the
shapes of  the  image isophotes  are  studied, diffusion  scale-spaces  that
examine the  linear or  non-linear mean  value of  neighbouring pixels,  and
statistical  methods   that   study   the   full   distribution   of   local
neighbourhoods.  Seemingly far apart, these views  are currently converging.
Morphological erosion  and dilation  scale-spaces  and the  linear-diffusion
scale-space  are  related  through   a  non-linear  transformation   of  the
grey-values.  Additionally, the notion of locally disorderly images has been
introduced,  that  facilitates  the  availability  of  the  simple  Gaussian
structure for  local statistical  analysis.  The goal  of this  paper is  to
reveal the relation between grey-value transforms and local soft histograms,
and to suggest a generalised scale-selection mechanism.


                                                       Host: Kim Skak Larsen