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Curves and Surfaces
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The reconstruction of surfaces from apparent contours
 In this project we aim to recover
the shape of arbitrary surfaces from the apparent contours (or outlines)
visible from arbitrary views. A key contribution is the introduction of the
epipolar parametrization which exploits the epipolar geometry (geometry of
viewpoints) to induce a spatio-temporal parametrization of the image curves
and surfaces. This generalizes the epipolar geometry of points to curves and
surfaces, and allows the recovery of shape under perspective projection and
arbitrary camera motion.
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The analysis of the degeneracies of the epipolar
parametrization
 Singular apparent contours or
cusps occur at isolated points, seen as an abrupt contour ending in the
outline of an opaque surface. The epipolar parametrization cannot be used to
recover surface geometry at these points. In this project, the locus of
cusps under viewer motion is exploited to recover the geometry in the
vicinity of the cusps. The other case of degeneracy is used to develop an
algorithm to recover viewer motion.
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Recovery of camera motion from outlines
 It is generally believed that the
outlines of a curved surface cannot be used to recover the motion since they
are projections of curves which slip over the surface under viewer motion.
In this project, the envelope of consecutive contour generators is shown to
define special (frontier) points and these points are used to recover the
epipolar geometry from image curves.
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Circular motion
 In this project, a particularly
simple and elegant solution is found for a special type of motion in which
an object is placed on a turntable which is rotated in front of a stationary
camera. A novel solution is introduced which exploits the symmetry in the
envelope of outlines swept out by the rotating surface. This technique uses
a single curve tracked over the image sequence and has been successfully
used to recover the shape of an arbitrary object from an uncalibrated
camera.
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Quasi-invariant parametrizations and matching of curves
 In this project we aim to develop
a robust algorithm for curves mathcing. B-splines can be fitted
automatically to image edge data and used to group fragments of curves which
are projections of bilateral symmetry in the scene. Quasi-invariant
parametrizations of image curves are developed to help in the matching of
curves. These reduce the order of derivatives required to compute the
geometric invariants of curves from fifth to second-order making these less
sensitive to image noise and occlusion.
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Visually Guided Robots
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Visual servoing
 This project takes advantage of
the geometric structure of the Lie algebra of the affine transformation. A
novel approach for visual servoing exploits a single robot motion to image
deformation Jacobian, computed once near the target location, to guide the
robot over a large range of perturbations. This framework has been extended
recently to produce a robust 3D model tracking system which is able to track
articulated objects in the presence of occlusion live from video images.
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2˝D Visual Servoing from Planar Contours
 The aim of this research is to
design a complete system for segmenting, matching and tracking planar
contours for use in visual servoing. Our system can be used with arbitrary
contours of any shape and without any prior knowledge of their models. The
system is first shown the target view. A selected contour is automatically
extracted and its image shape is stored. The robot and object are then moved
and the system automatically identifies the target. The matching step is
done together with the estimation of the homography matrix between the two
views of the contour. Then, a 2˝D visual servoing technique is used to
reposition the end-effector of a robot at the target position relative to
the planar contour. The system has been successfully tested on several
contours with very complex shapes such as leaves, keys and the coastal
outlines of islands.
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Image Divergence from Closed Curves
 Visual motion, as perceived by a
camera mounted on a robot moving relative to a scene, can be used to aid in
navigation. Simple cues such as time to contact can in principle be
estimated from the divergence of the image velocity field. In practice
methods using spatio-temporal derivatives of image velocity were too
sensitive to image noise to be useful. This project considers the temporal
evolution of the apparent area of a closed contour (and an extension of
Green's theorem in the plane) and aims to recover time to contact and
surface orientation reliably. This is exploited in real-time visual docking
and obstacle avoidance.
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Uncalibrated Stereo Hand-Eye Coordination
 In this project, a simple and
robust approximation to stereo using only the cues available under
orthographic projection is used to build a system which exploits relative
disparity (and its gradient) in uncalibrated stereo to guide a robot
manipulator to pick up unfamiliar objects in an unstructured scene. The
system must not only be able to cope with uncertainty in shape of the
object, but also with uncertainty in the postions and orientations of the
camera, the robot and the object.
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Man-Machine Interfaces Using Visual Gestures, Pointing
 By detecting and tracking a human
hand the system is extended so that the user can point at an object of
interest and guide the robotic manipulator to pick it up. The project uses
uncalibrated stereo vision and visual tracking of the hand. This makes the
system robust to movement of the cameras and of the user. This is just one
example of novel man-machine interfaces using computer vision to provide
more natural ways of interacting with computers and machines. Some of the
earliest examples in this field include a wireless, passive alternative to a
3D mouse which exploits motion parallax cues and an algorithm to detect and
track face gaze which exploits symmetry.
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Visual Tracking
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The Temporal Consensus Tracker
 The temporal consensus tracker
uses a minimal subset of the data to provide the pose estimate, and a robust
regression scheme to select the best subset. Bayesian inference in the
regression stage combines measurements taken in one frame with predictions
from previous frames, eliminating the need to further filter the pose
estimates. The resulting tracker performs very well on the difficult task of
tracking a human face, even when the face is partially occluded. Since the
tracker is tolerant of noise, computationally cheap feature detectors,
frame-rate operation is comfortably achieved on standard hardware.
The MPEG video below shows the temporal consensus algorithm tracking a human
face. The orientation of the face is illustrated as a drawing pin in the top
left hand corner of each frame.
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Automatic Human Face Detection and Localisation
 This project aims to achieve
automatic detection and localization of human faces in scenes with no prior
information about scale, orientation or viewpoint.
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3D Models from Images
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PhotoBuilder
 Aiming to build realistic 3D
models from photographs of architectural scenes, the PhotoBuilder
application reconstructs models from photographs taken from arbitrary
viewpoints.
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Photorealistic Models from uncalibrated photos
 Using a simple interactive
algorithm to generate the initial model and then refining this
automatically, the project aims to combine the best parts of automatic and
interactive 3D model creation.
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3D Television
 The purpose of this project is to
display photo-realistic three-dimensional images using off the shelf
cameras, and with minimal camera calibration.
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Image Segmentation and Grouping
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Motion Segmentation for Video Indexing
 A single video contains a vast
amount of information; the aim of video indexing is to automatically analyse
a video to extract a small amount of characteristic information.
Investigating the extraction of information from the motion in a scene,
techniques for segmentation and mosaicing are used to distil a description
of the scene that can be searched for.
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Image Segmentation
 The "creep and merge"
segmentation system aims to solve as many as possible of the reported
difficulties with segmentation systems, and produce a single, parameter-free
software package implementing the results.
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Human Face Detection
 This project aims to achieve
automatic detection and localization of human faces in scenes with no prior
information about scale, orientation or viewpoint.
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