General Electric Company

Siemens Medical Solutions USA, Inc.

Mirada Solutions Limited

Isis Innovation Limited

Julia Alison Noble

Richard I Hartley

Andrea Marie Schmitz

Rajiv Gupta

Miguel Mulet-Parada

Gary Jacob

An image processing technique which identifies pixels in images which are associated with features having a selected shape, such as but not exclusively step edge, roof, ridge or valley. The shape of the intensity profile in the image is compared in an intensity independent way with a shape model to select those pixels which satisfy the shape model and are thus associated with the feature of interest. This comparison is achieved by examining the phase and amplitude of a spectral decomposition of parts of the image profile in the spatial or spatio temporal frequency domain. This decomposition can be achieved using quadrature wavelet pairs such as log-Gabor wavelets. The difference between the odd and even components, known as the feature asymmetry, gives an indication of the shape of the feature. The analysis may be extended to the time domain by looking at the shape of the image profile across a time sequence of images, which gives an indication of the velocity of a moving feature. Pixels identified as belonging to a feature of the right shape are labelled with the value of feature asymmetry, the local amplitude, feature orientation and feature velocity, and this information can be used to improve the tracking of detected features through a sequence of images.

Detection of features in images

A method of analysing a sequence of images of a deformable object in non-rigid motion involves modeling the boundary using a non-rigid contour. A representation of movement of the contour through the sequence of images is calculated using a tracking space shape. The calculated movement representation is decomposed using an interpretational space shape that is different than the tracking space shape.

Non-rigid motion image analysis

A method of calibrating a linear pushbroom imaging device used in non-destructive testing of a part manufactured according to a CAD model involves identifying the location of fixed reference points, such as tooling balls in the CAD model. Positioning the part on a fixture having tooling balls fixed relative to each other in the same manner as those in the CAD model. Adjusting the part on the fixture such that points on the surface of the part known to be accurate have the same locations relative to the tooling bails as the CAD part is to the CAD tooling balls. Obtaining several projection images of the part and tooling balls at different angles about an axis of rotation passing through the part. Using the known 3D locations of the center of the CAD tooling balls and the corresponding measured screen locations of the is actual tooling balls, transformation matrices, G, M.sub.j may iteratively be determined which map points on the 3D CAD model to 3D points of the part and then map the 3D points of the part to the 2D screen coordinates of each projection image, respectively. This provides a method of determining theoretical correct point locations in a coordinate system fixed with respect to the part, and theoretically correct screen coordinates in a reference frame fixed with respect to the imaging device.

Method of calibration of imaging devices

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Oxford, United Kingdom

Julia Alison Noble