AISB Convention 2015

The AISB Convention is an annual conference covering the range of AI and Cognitive Science, organised by the Society for the Study of Artificial Intelligence and Simulation of Behaviour. The 2015 Convention will be held at the Uni...


Read More...

Yasemin Erden on BBC

AISB Committee member, and Philosophy Programme Director and Lecturer, Dr Yasemin J. Erden interviewed for the BBC on 29 October 2013. Speaking on the Today programme for BBC Radio 4, as well as the Business Report for BBC world N...


Read More...

Mark Bishop on BBC ...

Mark Bishop, Chair of the Study of Artificial Intelligence and the Simulation of Behaviour, appeared on Newsnight to discuss the ethics of ‘killer robots’. He was approached to give his view on a report raising questions on the et...


Read More...

AISB YouTube Channel

The AISB has launched a YouTube channel: http://www.youtube.com/user/AISBTube (http://www.youtube.com/user/AISBTube). The channel currently holds a number of videos from the AISB 2010 Convention. Videos include the AISB round t...


Read More...

Lighthill Debates

The Lighthill debates from 1973 are now available on YouTube. You need to a flashplayer enabled browser to view this YouTube video  


Read More...
01234

Notice

AISB opportunities Bulletin Item

PhD Scholarship in Robust Statistical Fitting for Computer Vision Applications, Swinburne, Australia


Contact: abab-hadiashar@swin.edu.au

Swinburne University of Technology, Faculty of Engineering and 
Industrial Science,
Contact: Assoc. Prof. Ali Bab-Hadiashar (abab-hadiashar@swin.edu.au)

The aim of this project is to study the essential (and ubiquitous) 
problem of automatically segmenting visual data into meaningful parts - 
a form of model fitting: but in the face of multiple objects, missing 
and noisy data, large data volumes, and unknown form (model) and number 
of objects. These latter characteristics lift the problem beyond the 
reach of standard statistical fitting approaches. There have been 
studies concentrating on optimal fitting (Kanatani 1996) (ignoring such 
things as outliers, multiple structures, data sample density etc.), or 
on robust fitting in the presence of multiple structures ((Meer 2004) 
not only provides a good overview if these approaches but also a good 
discussion of why traditional robust statistics is inadequate for such a 
setting) but generally ignoring small sample issues, model selection, 
and a plethora of other practical issues. This project is an ambitious 
attempt to provide a more complete theory and practical methodology.

(Kanatani 1996) K. Kanatani, Statistical Optimization for Geometric 
Computation: Theory and Practice, Elsevier Science, Amsterdam, The 
Netherlands, 1996.
(Meer 2004) P. Meer: Robust techniques for computer vision. Emerging 
Topics in Computer Vision, G. Medioni and S. B. Kang (Eds.), Prentice 
Hall, 107-190, 2004.

The project will focus on range data (i.e., laser scan and 3D 
reconstruction from images) and motion estimation/segmentation. It will 
build upon our previous work in this area (see references below).

A. Bab-Hadiashar, N. Gheissari. Range Image Segmentation Using Surface 
Selection Criterion, IEEE Transaction on Image Processing, 15(7), 
2006-2018, 2006.
N. Gheissari, A. Bab-Hadiashar, and D. Suter. Parametric model-based 
motion segmentation using surface selection  criterion. Computer Vision 
and Image Understanding, 102(2):214-226, 2006.
K. Schindler and D. Suter. Two-view multibody structure-and-motion with 
outliers through model selection. IEEE Trans. Pattern Analysis and 
Machine Intelligence, 28(6):983-995, 2006.
P. Chen and D. Suter. An analysis of linear subspace approaches for 
computer vision and pattern recognition. International Journal of 
Computer Vision, 68(1):83-106, 2006.
R. Hoseinnezhad, A. Bab-Hadiashar, and D. Suter. Finite sample bias of 
robust scale estimators in computer vision problems. In Lecture Notes in 
Computer Science, International Symposium on Visual Computing (ISVC06), 
volume 4291, pages 445-454, Heidelberg, 2006.  Springer-Verlag.

Candidates should have engineering or science background (with strong 
mathematical and statistical skills) and experience in image analysis 
and software development would be beneficial.