André Oosterlinck

Texture analysis anno 1983

Abstract In this paper the texture analysis methods being used at present are reviewed. Statistical as well as structural approaches are included and their performances are compared. Concerning the former approach, the gray level difference method, filter mask texture measures, Fourier power spectrum analysis, cooccurrence features, gray level run lengths, autocorrelation features, methods derived from texture models, relative extrema measures, and gray level profiles are discussed. Structural methods which describe texture by its primitives and some placement rules are treated as well. Attention has to be paid to some essential preprocessing steps and to the influence of rotation and scale on the texture analysis methods. Finally the problem of texture segmentation is briefly discussed.

Speckle filtering of synthetic aperture radar images: A review

Abstract Speckle, appearing in synthetic aperture radar (SAR) images as granular noise, is due to the interference of waves reflected from many elementary scatterers. Speckle in SAR images complicates the image interpretation problem by reducing the effectiveness of image segmentation and classification. To alleviate deleterious effects of speckle, various ways have been devised to suppress it. This paper surveys several better‐known speckle filtering algorithms. The concept of each filtering algorithm and the interrelationship between algorithms are discussed in detail. A set of performance criteria is established and comparisons are made for the effectiveness of these filters in speckle reduction and edge, line, and point target contrast preservation using a simulated SAR image as well as airborne and spaceborne SAR images. In addition, computational efficiency and implementation complexity are compared. This critical evaluation of speckle suppression filters is mostly new and is presented as a survey p...

Determination of optical flow and its discontinuities using non-linear diffusion

A new method for optical flow computation by means of a coupled set of non-linear diffusion equations is presented. This approach integrates the classical differential approach with the correlation type of motion detectors. A measure of inconsistency within the optical flow field which indicates optical flow boundaries. This information is fed back to the optical flow equations in a non-linear way and allows the flow field to be reconstructed while preserving the discontinuities. The whole scheme is also applicable to stereo matching. The model is applied to a set of synthetic and real image sequences to illustrate the behaviour of the coupled diffusion equations.

Foundations of semi-differential invariants

This paper elaborates the theoretical foundations of a semi-differential framework for invariance. Semi-differential invariants combine coordinates and their derivatives with respect to some contour parameter at several points of the image contour, thus allowing for an optimal trade-off between identification of points and the calculation of derivatives. A systematic way of generating complete and independent sets of such invariants is presented. It is also shown that invariance under reparametrisation can be cast in the same framework. The theory is illustrated by a complete analysis of 2D affine transformations. In a companion paper (Pauwels et al. 1995) these affine semi-differential invariants are implemented in the computer program FORM (Flat Object Recognition Method) for the recognition of planar contours under pseudo-perspective projection.

Recognition and semi-differential invariants

Semidifferential invariants, combining coordinates in different points together with their derivatives, are used for the description of planar contours. Their use can be seen as a tradeoff between two extreme strategies currently used in shape recognition: (invariant) feature extraction methods, involving high-order derivatives, and invariant coordinate descriptions, leading to the correspondence problem of reference points. The method for the derivation of such invariants, based on Lie group theory and applicable to a wide spectrum of transformation groups, is described. As an example, invariant curve parameterizations are developed for affine and projective transformations. The usefulness of the approach is illustrated with two examples: (1) recognition of a test set of 12 planar objects viewed under conditions allowing affine approximations, and (2) the detection of symmetry in perspective projections of curves.<<ETX>>

Active acquisition of 3D shape for moving objects

An active 3D acquisition system is presented that projects a simple pattern of squares on a scene and views it from a different angle. This paper describes how the observed pattern can be extracted from the image data. The underlying algorithm automatically detects the lines and crossings of the projected pattern in the image. Experiments show that the algorithm is robust and provides accurate three-dimensional (3D) reconstructions. Its one-shot operation principle enables the system to retrieve the shape of moving objects.

Recognition of planar shapes under affine distortion

Methods for the recognition ofplanar shapes from arbitrary viewpoints are described. The adopted model of projection is orthographic. The invariant descriptions derived for this group are one-dimensional shape signatures comparable to the well-known curvature as a function of arc length description of Euclidean geometry. Since the use of such differential invariants in the affine case would lead to unacceptably high orders of derivatives, affine invariant descriptions based onsemi-differential invariants are proposed as an alternative. A systematic discussion of different types of these invariants is given. The usefulness and viability of this methodology is demonstrated on a database containing more than 40 objects.

One-shot active 3D shape acquisition

An active 3D acquisition system is presented that is based on the projection a simple pattern of squares. Through the observation of the projected pattern, and assuming pseudo-orthographic projection, the 3D shape of the scene can be retrieved up to a single parameter, that can be determined through a particularly simple calibration. An algorithm has been developed to automatically detect the lines and crossings of the projected pattern in the image. Experiments on a variety of scenes show that the algorithm is robust and provides accurate 3D reconstructions. Its one-shot operation enables the system to retrieve the shape of moving objects.

Delineation of ECT images using global constraints and dynamic programming

A model-based delineation algorithm is presented. It is a flexible model fitting algorithm, approaching contour detection as an optimization problem. An objective function is introduced, which depends not only on local contour features, but also on a global shape constraint. The latter is implemented as the similarity to the instance of a parametric shape model. The algorithm optimizes both the contour points and the parameters of the model. As a result, both global and local characteristics of the contour are determined as a compromise between photometric data and prior knowledge. The method was applied to myocardial perfusion SPECT images, to delineate the entire left ventricle (endocardium and epicardium), including possible regions of reduced perfusion. By adapting the balance between the image data and the shape model, images with different characteristics can be processed, including Thallium-201 and MIBI scans.

The characterization and detection of skewed symmetry

Abstract Skewed symmetry is the type of pattern that emerges when viewing a mirror symmetric, planar shape obliquely. This paper discusses the orthographic case, but also briefly comments on perspective skewing. Orthographically skewed symmetry is characterized by two features which are also present in perfect mirror symmetry and that are preserved under the skewing, i.e., that are invariant under affine transformations. These are parallelism of the chords, the lines joining corresponding points on each side of the symmetric shape are parallel; and collinearity of the midpoints, the points lying in the middle of each of the corresponding point pairs are collinear so that they form a straight symmetry axis. These two constraints (the parallelism constraint and the collinearity constraint) are taken as point of departure and it is shown how they relate to a set of invariants, which skew mirrored point pairs or contour segments should satisfy if they are to make up corresponding point pairs of a skewed symmetry. Although a method based on such invariants is presented, the major outcome is that the previous constraint pair is equivalent to another one, which does not require a priori knowledge of point correspondence.