S. Hamid Rezatofighi
PhD Student of Engineering & Computer Science
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About me
Welcome to my personal homepage. Since March 2011, I am a PhD student of Eng. & Computer Sci. at the Australian National University. I also received my master degree from the University of Tehran in February 2009.
* (10-2013) I will be giving a talk about "Multi-target tracking using random finite set theory" at the Applied Signal Processing group, ANU on Nov. 14th 2013.
* (05-2013) I will be visiting Technical University of Munich from 18 May to 12 August.
* (02-2013) I have a paper accepted in IPMI 2013, selected for oral presentation.
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* (05-2013) I will be visiting Technical University of Munich from 18 May to 12 August.
* (02-2013) I have a paper accepted in IPMI 2013, selected for oral presentation.
[See more...]
My research interests include medical signal and image analysis and computer vision and my thesis title is "Multi-target tracking in time-lapse cell microscopy sequences".
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Affiliation
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College Eng. & Computer Sci., ANU, Australia
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CSIRO Computational Informatics, Australia
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CSIRO Computational Informatics, Australia
Collaboration
Yale School of Medicine, USA
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CAMP, Technical Univ. of Munich, Germany
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Helmholtz Assoc. of Research Ctr, Germany
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ECE, Curtin University., Australia
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Garvan Institute of Medical Research, Australia
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CAMP, Technical Univ. of Munich, Germany
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Helmholtz Assoc. of Research Ctr, Germany
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ECE, Curtin University., Australia
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Garvan Institute of Medical Research, Australia
Recent projects
SEGMATION OF VASCULAR STRUCTURES
MULTI-TARGET TRACKING IN TIME-LAPSE MICROSCOPY
Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions.
In this project, we focus on tracking these structures in different biological applications based on Bayesian filters.
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Despite significant technical advances made in automatically segmentation of tubular structures, the vessel segmentation still remains a challenging task in many practical applications due to the complex nature of the vascular networks. These networks usually includes numerous branching and bifurcations in where many segmentation algorithms fails to work properly.
Moreover, the vessels may not be distinguished well from its surroundings due to low image contrast, noise, vicinity to other structures with similar brightness and other pathological reason. This project provides a novel perspective to the vascular centerline tracking and segmentation problem using Bayesian approach.