Dr. Andrew Newell

PhD Title:
Invariant encoding schemes for visual recognition

PhD Abstract:
Many encoding schemes, such as the Scale Invariant Feature Transform (SIFT) and Histograms of Oriented Gradients (HOG), make use of templates of histograms to enable a loose encoding of the spatial position of basic features such as oriented gradients. Whilst such schemes have been successfully applied, the use of a template may limit the potential as it forces the histograms to conform to a rigid spatial arrangement. In this work we look at developing novel schemes making use of histograms, without the need for a template, which offer good levels of performance in visual recognition tasks.

To do this, we look at the way the basic feature type changes across scale at individual locations. This gives rise to the notion of column features, which capture this change across scale by concatenating feature types at a given scale separation. As well as applying this idea to oriented gradients, we make wide use of Basic Image Features (BIFs) and oriented Basic Image Features (oBIFs) which encode local symmetry information. This resulted in a range of encoding schemes.

We then tested these schemes on problems of current interest in three application areas. First, the recognition of characters taken from natural images, where our system outperformed existing methods. For the second area we selected a texture problem, involving the discrimination of quartz grains using surface texture, where the system achieved near perfect performance on the first task, and a level of performance comparable to an expert human on the second. In the third area, writer identification, the system achieved a perfect score and outperformed other methods when tested using the Arabic handwriting dataset as part of the ICDAR 2011 Competition.

Next employment after CoMPLEX:
Research Fellow at UCL, Department of Computer Science


Relevant publications:

  • A. J. Newell, L. D. Griffin, R. M. Morgan, and P. A. Bull. Texture-based estimation of physical characteristics of sand grains. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA) Sydney, pages 504–509, 2010.
  • A. J. Newell and L.D. Griffin. Multiscale histogram of oriented gradient descriptors for robust character recognition. In Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2011.
  • A. D. F. Clarke, F. Halley, A. J. Newell, L. D. Griffin, and M. J. Chantler. Perceptual similarity: A texture challenge. In Proceedings of the British Machine Vision Conference (BMVC), 2011.
  • A. J. Newell and L.D. Griffin. Natural image character recognition using oriented Basic Image Features. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2011.
  • A. J. Newell, R. M. Morgan, L. D. Griffin, P. A. Bull, J.R. Marshall, and G. Graham. Automated texture recognition of quartz sand grains for forensic applications. Journal of Forensic Sciences 2012

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