Cambridge University Engineering Department

	Department of Engineering

University of Cambridge > Engineering Department

Paul McIlroy

PhD student supervised by Professor Roberto Cipolla and Dr Ed Rosten
in the Machine Intelligence Laboratory at CUED.
Also a Munro Student and Part IA (Maths & Electrical) supervisor at Queens' College.
Intern at Microsoft Research Cambridge

First two years of PhD supervised by Professor Tom Drummond


	High-level Scene Structure using Visibility and Occlusion McIlroy, Cipolla and Rosten This paper proposes a new method for extracting high-level scene information from the type of data available from simultaneous localisation and mapping systems. The scene is modelled using a collection of primitives (such as bounded planes) and explicit use is made of both visible and occluded points in order to refine the model. Since the formulation allows for different kinds of primitives and an arbitrary number of each, Bayesian model evidence is used to compare very different models on an even footing. The results show that explicit reasoning about occlusion improves model accuracy and yields models which are suitable for aiding data association. [2011 BMVC Abstract] [2011 BMVC Paper]

High-level Scene Structure using Visibility and Occlusion
McIlroy, Cipolla and Rosten

This paper proposes a new method for extracting high-level scene information from the type of data available from simultaneous localisation and mapping systems. The scene is modelled using a collection of primitives (such as bounded planes) and explicit use is made of both visible and occluded points in order to refine the model. Since the formulation allows for different kinds of primitives and an arbitrary number of each, Bayesian model evidence is used to compare very different models on an even footing. The results show that explicit reasoning about occlusion improves model accuracy and yields models which are suitable for aiding data association.

[2011 BMVC Abstract] [2011 BMVC Paper]


	Deterministic Sample Consensus with Multiple Match Hypotheses McIlroy, Rosten, Taylor and Drummond This paper proposes a deterministic scheme for selecting correspondences from feature matching to generate motion hypotheses. The method combines matching scores, ambiguity and the past performance of motion hypotheses generated by the matches, to estimate the probability that a feature match is correct. At every stage the best correspondences are chosen to generate a hypothesis. This method will therefore only spend time on poor or ambiguous matches when the best correspondences have proven themselves to be unsuitable. The result is a system that is able to operate efficiently on ambiguous data and is suitable for implementation on devices with limited computing resources. [2010 BMVC Abstract] [2010 BMVC Paper]

Deterministic Sample Consensus with Multiple Match Hypotheses
McIlroy, Rosten, Taylor and Drummond

This paper proposes a deterministic scheme for selecting correspondences from feature matching to generate motion hypotheses. The method combines matching scores, ambiguity and the past performance of motion hypotheses generated by the matches, to estimate the probability that a feature match is correct. At every stage the best correspondences are chosen to generate a hypothesis. This method will therefore only spend time on poor or ambiguous matches when the best correspondences have proven themselves to be unsuitable. The result is a system that is able to operate efficiently on ambiguous data and is suitable for implementation on devices with limited computing resources.

[2010 BMVC Abstract] [2010 BMVC Paper]


	Reconstruction from Uncalibrated Affine Silhouettes McIlroy and Drummond This work addresses the problem of model building from multiple affine silhouette views of an object in an uncontrolled environment such as an aircraft in flight. Each pair of silhouette views provides two outer epipolar tangency constraints on the relative motion between the cameras. For a scaled orthographic camera model with six degrees of freedom we show that it is possible to recover structure and motion from six or more silhouette views by solving the outer epipolar tangency constraints simultaneously. [More] [2009 BMVC Abstract] [2009 BMVC Paper]

Reconstruction from Uncalibrated Affine Silhouettes
McIlroy and Drummond

This work addresses the problem of model building from multiple affine silhouette views of an object in an uncontrolled environment such as an aircraft in flight. Each pair of silhouette views provides two outer epipolar tangency constraints on the relative motion between the cameras. For a scaled orthographic camera model with six degrees of freedom we show that it is possible to recover structure and motion from six or more silhouette views by solving the outer epipolar tangency constraints simultaneously. [More]

[2009 BMVC Abstract] [2009 BMVC Paper]

Contact Information

Email:	Paul McIlroy (pmm33)
Address:	Department of Engineering. University of Cambridge Trumpington Street, Cambridge CB2 1PZ
Telephone:	+44 1223 332 748
Fax:	+44 1223 332 662

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