I have worked on various theoretical aspects of Pattern Recognition, Image Analysis and Computer Vision, and on many applications including System Identification, Automatic Inspection, ECG diagnosis, Mammographic Image Interpretation, Remote Sensing, Robotics, Speech Recognition, Character Recognition and Document Processing, Image Coding, Biometrics, Image and Video Database Retrieval, Surveillance. Contributions to statistical pattern recognition include k-nearest neighbour methods of pattern classification, feature selection, contextual classification, probabilistic relaxation and most recently to multiple expert fusion. In computer vision my major contributions include robust statistical methods for shape analysis and detection, motion estimation and segmentation, and image segmentation by thresholding and edge detection.
I have co-authored a book with the title `Pattern Recognition: a statistical approach' published by Prentice-Hall and published more than 500 papers.
Served as a member of the Editorial Board of IEEE Transactions on Pattern Analysis and Machine Intelligence during 1982-85. Currently serves on the Editorial Boards of Pattern Recognition Letters, Pattern Recognition and Artificial Intelligence, Pattern Analysis and Applications.
Served on the Governing Board of the International Association for Pattern Recognition (IAPR) as one of the two British representatives during the period 1982-2005. President of the IAPR during 1994-1996. Currently a member of the KS Fu Prize Committee of IAPR.
Received Best Paper awards from the Pattern Recognition Society, the British Machine Vision Association and IEE.
Received ``Honorary Medal'' from the Electrotechnical Faculty of the Czech Technical University in Prague in September 1995 for contributions to the field of pattern recognition and computer vision.
Elected Fellow of the International Association for Pattern Recognition in 1998
Elected Fellow of Institution of Electrical Engineers in 1999
Received Honorary Doctorate from the Lappeenranta University of Technology, Finland, for contributions to Pattern Recognition and Computer Vision in 1999
Elected Fellow of the Royal Academy of Engineering, 2000
Received Institution of Electrical Engineers Achievemments Medal 2002 for outstanding contributions to Visual Information Engineering
Elected BMVA Distinguished Fellow 2002
Received, from the Czech Academy of Sciences, the 2003 Bernard Bolzano Honorary Medal for Merit in the Mathematical Sciences
Awarded the title Distinguished Professor of the University of Surrey in 2004
Appointed as Series Editor for Springer Lecture Notes in Computer Science 2004
Awarded the KS Fu Prize 2006, by the International Association for Pattern Recognition, for outstanding contributions to Pattern Recognition (the prize awarded biennially)
Received Honorary Doctorate from the Czech Technical University in Prague in 2007, on the occasion of the 300th anniversary of its foundation.
Awarded the IET Faraday Medal 2008.
Find me on campus Room: 38 AB 05
Automation of HEp-2 cell pattern classification would drastically improve the accuracy and throughput of diagnostic services for many auto-immune diseases, but it has proven difficult to reach a sufficient level of precision. Correct diagnosis relies on a subtle assessment of texture type in microscopic images of indirect immunofluorescence (IIF), which has, so far, eluded reliable replication through automated measurements. Following the recent HEp-2 Cells Classification contest held at ICPR 2012, we extend the scope of research in this field to develop a method of feature comparison that goes beyond the analysis of individual cells and majority-vote decisions to consider the full distribution of cell parameters within a patient sample. We demonstrate that this richer analysis is better able to predict the results of majority vote decisions than the cell-level performance analysed in all previous works. © 2013.
Lip region deformation during speech contains biometric information and is termed visual speech. This biometric information can be interpreted as being genetic or behavioral depending on whether static or dynamic features are extracted. In this paper, we use a texture descriptor called local ordinal contrast pattern (LOCP) with a dynamic texture representation called three orthogonal planes to represent both the appearance and dynamics features observed in visual speech. This feature representation, when used in standard speaker verification engines, is shown to improve the performance of the lip-biometric trait compared to the state-of-the-art. The best baseline state-of-the-art performance was a half total error rate (HTER) of 13.35% for the XM2VTS database. We obtained HTER of less than 1%. The resilience of the LOCP texture descriptor to random image noise is also investigated. Finally, the effect of the amount of video information on speaker verification performance suggests that with the proposed approach, speaker identity can be verified with a much shorter biometric trait record than the length normally required for voice-based biometrics. In summary, the performance obtained is remarkable and suggests that there is enough discriminative information in the mouth-region to enable its use as a primary biometric trait. © 2006 IEEE.
Sparsity-inducing multiple kernel Fisher discriminant analysis (MK-FDA) has been studied in the literature. Building on recent advances in non-sparse multiple kernel learning (MKL), we propose a non-sparse version of MK-FDA, which imposes a general `p norm regularisation on the kernel weights. We formulate the associated optimisation problem as a semi-infinite program (SIP), and adapt an iterative wrapper algorithm to solve it. We then discuss, in light of latest advances inMKL optimisation techniques, several reformulations and optimisation strategies that can potentially lead to significant improvements in the efficiency and scalability of MK-FDA. We carry out extensive experiments on six datasets from various application areas, and compare closely the performance of `p MK-FDA, fixed norm MK-FDA, and several variants of SVM-based MKL (MK-SVM). Our results demonstrate that `p MK-FDA improves upon sparse MK-FDA in many practical situations. The results also show that on image categorisation problems, `p MK-FDA tends to outperform its SVM counterpart. Finally, we also discuss the connection between (MK-)FDA and (MK-)SVM, under the unified framework of regularised kernel machines.
Performing facial recognition between Near Infrared (NIR) and visible-light (VIS) images has been established as a common method of countering illumination variation problems in face recognition. In this paper we present a new database to enable the evaluation of cross-spectral face recognition. A series of preprocessing algorithms, followed by Local Binary Pattern Histogram (LBPH) representation and combinations with Linear Discriminant Analysis (LDA) are used for recognition. These experiments are conducted on both NIR→VIS and the less common VIS→NIR protocols, with permutations of uni-modal training sets. 12 individual baseline algorithms are presented. In addition, the best performing fusion approaches involving a subset of 12 algorithms are also described. © 2011 IEEE.
We describe a novel framework to detect ball hits in a tennis game by combining audio and visual information. Ball hit detection is a key step in understanding a game such as tennis, but single-mode approaches are not very successful: audio detection suffers from interfering noise and acoustic mismatch, video detection is made difficult by the small size of the ball and the complex background of the surrounding environment. Our goal in this paper is to improve detection performance by focusing on high-level information (rather than low-level features), including the detected audio events, the ball’s trajectory, and inter-event timing information. Visual information supplies coarse detection of the ball-hits events. This information is used as a constraint for audio detection. In addition, useful gains in detection performance can be obtained by using and inter-ballhit timing information, which aids prediction of the next ball hit. This method seems to be very effective in reducing the interference present in low-level features. After applying this method to a women’s doubles tennis game, we obtained improvements in the F-score of about 30% (absolute) for audio detection and about 10% for video detection.
We consider the problem of learning a linear combination of pre-specified kernel matrices in the Fisher discriminant analysis setting. Existing methods for such a task impose an l norm regularisation on the kernel weights, which produces sparse solution but may lead to loss of information. In this paper, we propose to use l norm regularisation instead. The resulting learning problem is formulated as a semi-infinite program and can be solved efficiently. Through experiments on both synthetic data and a very challenging object recognition benchmark, the relative advantages of the proposed method and its l counterpart are demonstrated, and insights are gained as to how the choice of regularisation norm should be made. © 2009 IEEE.
Page Owner: ees1jk
Page Created: Thursday 5 August 2010 15:44:10 by lb0014
Last Modified: Wednesday 1 October 2014 16:06:33 by eex1kg
Expiry Date: Thursday 18 August 2011 10:40:18
Assembly date: Fri Jan 30 20:20:13 GMT 2015
Content ID: 32831