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AISB opportunities Bulletin Item

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


Swinburne University of Technology, Faculty of Engineering and 
Industrial Science,
Contact: Assoc. Prof. Ali Bab-Hadiashar (

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.