Goesta H. Granlund

Anisotropic Nonstationary Image Estimation and Its Applications: Part I--Restoration of Noisy Images

A new form of image estimator, which takes account of linear features, is derived using a signal equivalent formulation. The estimator is shown to be a nonstationary linear combination of three stationary estimators. The relation of the estimator to human visual physiology is discussed. A method for estimating the nonstationary control information is described and shown to be effective when the estimation is made from noisy data. A suboptimal approach which is computationally less demanding is presented and used in the restoration of a variety of images corrupted by additive white noise. The results show that the method can improve the quality of noisy images even when the signal-to-noise ratio is very low.

The Uncertainty Principle in Image Processing

The uncertainty principle is recognized as one of the fundamental results in signal processing. Its role in inference is, however, less well known outside of quantum mechanics. It is the aim of this paper to provide a unified approach to the problem of uncertainty in image processing. It is shown that uncertainty can be derived from the fundamental constraints on the process of vision-the requirements for class-defining operations which are both shift-invariant and insensitive to changes in illumination. It is thus shown that uncertainty plays a key role in the language of vision, since it affects the choice of both the alphabet, the elementary signals, and the syntax, the inferential structure, of vision. The report is concluded with a number of practical illustrations of these ideas, taken from such image processing tasks as enhancement, data compression, and segmentation.

Organization of Architectures for Cognitive Vision Systems

The purpose of cognitive systems is to produce a response to appropriate percepts. The response may be a direct physical action which may change the state of the system. It may be delayed in the form of a reconfiguration of internal models in relation to the interpreted context of the system. Or it may be to generate in a subsequent step a generalized symbolic representation which will allow its intentions of actions to be communicated to some other system. As important as the percepts, is the dependence upon context.

Ectomography-A New Radiographic Reconstruction Method-I. Theory and Error Estimates

Radiographic technology has advanced considerably during the last years with the advent of reconstruction techniques allowing visualization of slices through the body. In spite of the advantage of computed tomography compared to conventional radiographic methods, there are still some shortcomings with the method If a different section of the body is desired, another recording has to be made, the width of the dice reconstructed is fixed, and a full 1800 view angle is required.

Ectomography-A New Radiographic Reconstruction Method-II. Computer Simulated Experiments

In a special radiographic process, ectomography, an image of a slice is produced by simple summation of a set of specially filtered component images, of which each represents one of at least 60 different projections of the object. After being digitized, they are stored, filtered, and summed in a computer. Images representing any slice of any thickness in the object may be produced from the same set of component images. All details within the slice are pictured correctly while details outside are almost completely eliminated.

The GOP Parallel Image Processor

Images contain a great deal of information which requires large processing capabilities. For that purpose fast image processors have been developed. So far they have mainly dealt with processing of binary images obtained by thresholding gray scale images. For segmentation of images having more subtle features such as noisy lines or edges, texture, color, etc. more elaborate procedures have to be used.

Hierarchical processing of structural information in artificial intelligence

Most problems in signal processing have structural aspects which are difficult to solve using general methods. Use of ad hoc methods is limited to cases where structural aspects are easy to re-solve. An approach for hierarchical processing is described whereby structural aspects are resolved simultaneously with the analysis of data values. An effective use of a hierarchical structure puts strong restrictions upon information representation and operations. Information is represented in terms of compatibility and incompatibility of events, combined with a measure of confidence. Operations are of type symmetry operations, which allow data compression, context control and have a good descriptive power. These methods have been tested in various problems in image analysis and image processing with satisfactory results.

Adaptive reconstruction using multiple views

This paper introduces a novel algorithm for extracting the optical flow obtained from a translating camera in a static scene. Occlusion between objects is incorporated as a natural component in a scene reconstruction strategy by first evaluating and reconstructing the foreground and then excluding its influence on the partly occluded objects behind.

Hierarchical feature extraction for computer-aided analysis of mammograms

A framework for computer-aided analysis of mammograms is described. General computer vision algorithms are combined with application specific procedures in a hierarchical fashion. The system is under development and is currently limited to detection of a few types of suspicious areas. The image features are extracted by using feature extraction methods where wavelet techniques are utilized. A low-pass pyramid representation of the image is convolved with a number of quadrature filters. The filter outputs are combined according to simple local Fourier domain models into parameters describing the local neighborhood with respect to the model. This produces estimates for each pixel describing local size, orientation, Fourier phase, and shape with confidence measures associated to each parameter. Tentative object descriptions are then extracted from the pixel-based features by application specific procedures with knowledge of relevant structures in mammograms. The orientation, relative brightness and shape of the object are obtained by selection of the pixel feature estimates which best describe the object. The list of object descriptions is examined by procedures, where each procedure corresponds to a specific type of suspicious area, e.g., clusters of microcalcifications.

Hierarchical Image Processing

Most problems in image processing have structural aspects which are difficult to resolve using general methods. Use of heuristic methods is limited to cases where structural aspects are easy to resolve. An approach for hierarchical processing of images is described whereby structural aspects are resolved simultaneously with the analysis of data values. An effective use of a hierarchical structure puts strong restrictions upon information representation and operations. Information is represented in terms of compatibility of events, combined with a measure of confidence. Operations are of type symmetry operations, which allow data compression, context control and have a good descriptive power. The usefulness of these methods in image analysis and image processing is illustrated.