We present a method for accurate image registration and motion compensation in multidimensional signals, such as two-dimensional (2-D) X-ray images and three-dimensional (3-D) computed tomography/magnetic resonance imaging volumes. The method is based on phase from quadrature filters, which makes it robust to noise and temporal intensity variations. The method is equally applicable to signals of two, three or higher number of dimensions. We use parametric models, e.g., affine models, finite elements or local affine models with global regularization. Experimental results show high accuracy for 2-D and 3-D motion compensation.

We present a method for accurate image registration and motion estimation in multidimensional volumes, such as 3D CT and MR images. The method is based on phase from quadrature filters, which makes it insensitive to variations in luminance and other disturbance in the images. The theory is not restricted to any particular kind of motion model or number of dimensions. Experimental results for affine motions in 3D show high accuracy.

A neural network model based on pulse signals is presented. Pulsed signals are shown to give the model such properties as a simplified hardware implementation, the ability to use stochastic search techniques, and biological plausibility. In this model, there is also the possibility of building modular neural systems. Since information representation in pulsed signals has to be studied carefully, the stochastic representation is outlined and it is shown how generation of a signal and estimation of information are obtained. Several design issues have to be solved before it is possible to use a model of this kind, and solutions are proposed for synaptic multiplication, summation, and nonlinearity. It is also shown that reinforcement learning is the type of learning which is best suited for this type of model and that this does not interfere with the properties mentioned above. Initial simulations of the model show promising results

The author proposes a technique for incorporate motion compensation in a general 3-D waveform coding environment. Examples are given for 3-D subband and DCT (discrete cosine transform) coding where motion compensation yields significant improvements as compared to the same technique without motion compensation. It is concluded that, for low rates, 3-D motion-compensated subband coding is a very efficient technique for the coding of image sequences.>

To increase the performance of image coding of sequences it is beneficial to apply temporal prefiltering before coding to increase redundancy. To reduce the subjective impact of coding artifacts also temporal postfiltering can be applied after decoding. A problem with both of these techniques however (and of the more general techniques of image sequence enhancement using temporal filters) is that severe distortion may appear in moving objects. The author presents a method to avoid these artifacts of temporal filtering by introducing movement compensated temporal filtering. This technique yields an anisotropic, non-stationary 3-dimensional transfer function, which locally 'tracks' moving objects. The effect is that the problem of unsharp moving objects is significantly reduced, which allows for much lower cut-off frequencies than stationary filters do. The approach works both with FIR- and IIR-type filters. If a linear phase FIR filter is used for postfiltering, combined with a sampling rate increase, it can be seen as a motion compensated interpolator.>

In image analysis and image coding there have been an increased use of multiresolution images, called pyramids or quad trees. In a recent paper Burt and Adelson [2] achieved good results with a Laplacian (bandpass) pyramid to compress image data. One problem with the conventional approach is that too many dimensions (samples) are used to describe the original image. This paper describes a method to represent an image bandpass pyramid with exactly the dimensionality of the original image. Both rectangular and hexagonal sampling are considered.