Nick Pears (N. E. Pears) - Research Projects

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I work in the research area of Computer Vision and Pattern Recognition with my PhD students and post-doctoral research assistants (RAs). We have current research interests, areas that we wish to expand into, and past projects that we are happy to reopen listed below. If you are interested in working with me in any of these areas, for example, as a PhD student, please contact me by email.

More information on each project can be obtained by clicking the links below, including downloadable publications.

1.   Keypoint Extraction and Landmarking of 3D Face Meshes. We use machine learning techniques to learn the properties that best distinguish facial landmarks from their surrounding (non-neighbourhood) vertices. Key techniques included in Clement Creusot's PhD are learning using Linear Discriminant Analysis and Adaboost to determine how to combine local descriptors. For extracting global structure of landmark positions, both model fitting and hypergraph matching techniques are employed.

2.   3D Local Shape Descriptors. We have designed several new pose invariant, yet discriminating 3D surface descriptors and used these in applications of 3D facial landmarking (Marcelo Romero's PhD) and 3D pose normalisation (Tom Heseltine's PhD).

3.   3D SLAM for mobile robots. Recently we have begun to work on Simultaneous Localisation and Mapping for mobile robots using the Kinect 3D camera (Hao Sun's PhD). The work has started by characterising the performance of the Kinect and understanding how to improve systematic and random error performance for our SLAM application. Mapping and localisation experimentation will take place in the Department's new Robot Lab, headed by Professor Jon Timmis.

4.   3D Face Recognition. We have specialised 3D cameras to collect 3D face data. Previous work in Tom Heseltine's PhD resulted in the development of a successful face recognition system for near frontal views of cooperating subjects. Our current focus is to develop novel, robust techniques to recognise faces at a distance, with non-cooperating subjects. In such cases we may need to recognise a profile view, when we have a frontal view stored in the gallery.

5.   Vision-based HCI. We are particularly interested in the interaction between mobiles (eg. smartphones) and intelligent displays. We wish to build on our novel concept of registered displays, where a touch screen click on a mobile is projected onto a distant display. This allows you to interact on the mobile as if you were interacting directly on the distant display itself. The core technology to allow this to work is image registration, where a copy of the mobile's captured image of the distant display is transmitted to that display's PC via a wireless link (bluetooth or wifi) for matching.

6.   Vehicle Segmentation and Recognition. We are developing techniques to automatically recognise the make and model of cars, lorries, and vans from standard (2D) video. This is an extension of our TSB funded CLASSAC project.

7.   Human Action Recognition. We are developing techniques to automatically recognise human actions from standard (2D) video. This is an extension of our TSB funded VIDEOWARE project and also uses segmentation work developed in the TSB funded CLASSAC project.

Possible future projects

There are several research areas that we wish to apply our expertise in. Some of these are listed below and you may wish to investigate the possibility of doing a PhD in one of these areas.

Evolvable Vision. This project draws on Computer Vision, Evolutionary Computation and reconfigurable hardware.
3D Object Retrieval. We are generally interested in 3D shape representation and matching, with a view to 3D model searching on the internet.
3D Biometrics. We are generally interested in Biometrics, particularly those derived from 3D vision systems. For example, we can extend our techniques which we apply to human faces, to the hand and the ear.

Past projects, still of interest

Past projects that are still of interest and relevance are listed below in reverse chronological order. Many of these have been funded by TSB, DTI or EPSRC. Although these projects are not currently active, we welcome PhD applicants who wish to build on our experience in these areas,

CLASSAC. Vehicle recognition embedded in road-side cameras. (TSB funded)
VIDEOWARE. Real-time embedded vision, for gesture and human-action recognition. (DTI funded)
FAR. 3D imaging for face recognition. (DTI funded)
SLATS. Visual ground obstacle detection for civil aviation. (DTI funded)
Face recognition from standard 2D images.
VISNAV. Visual navigation for mobile robots. (EPSRC funded)

Nick Pears

1.		Keypoint Extraction and Landmarking of 3D Face Meshes. We use machine learning techniques to learn the properties that best distinguish facial landmarks from their surrounding (non-neighbourhood) vertices. Key techniques included in Clement Creusot's PhD are learning using Linear Discriminant Analysis and Adaboost to determine how to combine local descriptors. For extracting global structure of landmark positions, both model fitting and hypergraph matching techniques are employed.

2.		3D Local Shape Descriptors. We have designed several new pose invariant, yet discriminating 3D surface descriptors and used these in applications of 3D facial landmarking (Marcelo Romero's PhD) and 3D pose normalisation (Tom Heseltine's PhD).

3.		3D SLAM for mobile robots. Recently we have begun to work on Simultaneous Localisation and Mapping for mobile robots using the Kinect 3D camera (Hao Sun's PhD). The work has started by characterising the performance of the Kinect and understanding how to improve systematic and random error performance for our SLAM application. Mapping and localisation experimentation will take place in the Department's new Robot Lab, headed by Professor Jon Timmis.

4.		3D Face Recognition. We have specialised 3D cameras to collect 3D face data. Previous work in Tom Heseltine's PhD resulted in the development of a successful face recognition system for near frontal views of cooperating subjects. Our current focus is to develop novel, robust techniques to recognise faces at a distance, with non-cooperating subjects. In such cases we may need to recognise a profile view, when we have a frontal view stored in the gallery.

5.		Vision-based HCI. We are particularly interested in the interaction between mobiles (eg. smartphones) and intelligent displays. We wish to build on our novel concept of registered displays, where a touch screen click on a mobile is projected onto a distant display. This allows you to interact on the mobile as if you were interacting directly on the distant display itself. The core technology to allow this to work is image registration, where a copy of the mobile's captured image of the distant display is transmitted to that display's PC via a wireless link (bluetooth or wifi) for matching.
6.		Vehicle Segmentation and Recognition. We are developing techniques to automatically recognise the make and model of cars, lorries, and vans from standard (2D) video. This is an extension of our TSB funded CLASSAC project.
7.		Human Action Recognition. We are developing techniques to automatically recognise human actions from standard (2D) video. This is an extension of our TSB funded VIDEOWARE project and also uses segmentation work developed in the TSB funded CLASSAC project.