New invariant features are presented that can be used for the recognition of planar color patterns such as labels, logos, pictograms, etc., irrespective of the viewpoint or the illumination conditions, and without the need for error prone contour extraction. The new features are based on moments of powers of the intensities in the individual color bands and combinations thereof. These moments implicitly characterize the shape, the intensity and the color distribution of the pattern in a uniform manner. The paper gives a classification of all functions of such moments which are invariant under both affine deformations of the pattern (thus achieving viewpoint invariance) as well as linear changes of the intensity values of the color bands (hence, coping with changes in the irradiance pattern due to different lighting conditions and/or viewpoints). The discriminant power and classification performance of the new invariants for color pattern recognition is tested on a data set of images of outdoors advertising panels. A comparison to moment invariants presented in literature is included as well.

A method is presented to (semi-)automatically generate 3D models of urban buildings from aerial images. The process starts by selecting (with a mouse click) one or more roofs in the image(s). The program then automatically delineates the roof structures in the images and extracts straight line segments which are matched across different views and reconstructed in 3D. These 3D line segments are then grouped into coplanar sets and combined into polygonal shapes. Each polygon hypothesis is verified with respect to the 3D reconstruction and the original image data; and, if necessary, corrected accordingly. Subsequently, the polygons are combined into a polyhedral roof model. The emphasis here is on extracting the correct topology of the roof structure. Metric accuracy is obtained by back-projecting the model into the images and minimizing the total reprojection error. Finally, the building model is completed by adding vertical walls to the reconstructed roof. The approach is tested on a state-of-the-art dataset of aerial images of residential areas in Brussels.

Cartography and other applications of remote sensing have led to an increased interest in the (semi-)automatic interpretation of structures in aerial images of urban and suburban areas. Although these areas are particularly challenging because of their complexity, the degree of regularity in such man-made structures also helps to tackle the problems. The paper presents the iterated application of the Hough transform as a means to exploit such regularities. It shows how such 'Cascaded Hough Transform' (or CHT for short) yields straight lines, vanishing points, and vanishing lines. It also illustrates how the latter assist in improving the precision of the former. The examples are based on real aerial photographs.

Over recent years, symmetry research has shifted from the detection of affinely to perspectively skewed mirror symmetry. Also, links between invariance research and symmetry-specific geometric constraints have been established. The paper aims to contribute to both strands. Several sets of symmetry specific invariants are derived, that can be used in different situations, depending on the a priori assumptions made. It is also argued that all the results directly apply to the case of perspectively skewed point symmetry.

A method is described to recover the three-dimensional affine structure of a scene consisting of at least five points identified in two perspective views with a relative object-camera translation in between. When compared to the results for arbitrary stereo views, a more detailed reconstruction is possible using less information. The method presented only assumes that the two images are obtained by identical cameras, but no knowledge about the intrinsic parameters of the camera(s) or about the performed translation is assumed. By the same method, affine 3D reconstruction from a single view can be achieved for parallel structures. In that case, four points suffice for affine reconstruction.

thirionOesat.kuleuven.ac.be This paper presents a 3D reconstruction method without calibration. The reconstruction is affine and based on line correspondences between three images delivered by a camera observing a scene under translation.

The paper describes a systematic approach to the analysis of symmetries in planar shapes. The shapes can be observed from arbitrary viewpoints. Hence, the results encompass the case of skewed symmetries. In fact, skewing is assumed to arise from perspective distortions, whereas most of the literature restricts the analysis to affine skewing. The point of departure is the identification of structures that symmetries keep fixed in an image. These define subgroups of the projectivities, which in turn assume simpler invariants than their projective counterparts. These invariants allow both simpler and more selective detection and checking of symmetries.

Explores how the functional form of scale space filters is determined by a number of a priori conditions. In particular, if one assumes scale space filters to be linear, isotropic convolution filters, then two conditions (viz. recursivity and scale-invariance) suffice to narrow down the collection of possible filters to a family that essentially depends on one parameter which determines the qualitative shape of the filter. Gaussian filters correspond to one particular value of this shape-parameter. For other values the filters exhibit a more complicated pattern of excitatory and inhibitory regions. This might well be relevant to the study of the neurophysiology of biological visual systems, for recent research shows the existence of extensive disinhibitory regions outside the periphery of the classical center-surround receptive field of LGN and retinal ganglion cells (in cats). Such regions cannot be accounted for by models based on the second order derivative of the Gaussian. Finally, the authors investigate how this work ties in with another axiomatic approach of scale space operators which focuses on the semigroup properties of the operator family. The authors show that only a discrete subset of filters gives rise to an evolution which can be characterized by means of a partial differential equation.>

The three-dimensional structure of a scene consisting of at least five points whose images are identified in two perspective views obtained by a translating camera, can be reconstructed up to a 3D affine transformation. Hence, a more detailed reconstruction is possible using less information when compared to the results for arbitrary stereo pairs taken with uncalibrated, perspective cameras.

The potential of coupled nonlinear diffusion processes to provide an efficient and implementable mechanism capable of edge preserving smoothing and noise suppression is investigated. The basic idea is that several maps undergo coupled development towards an equilibrium state. These maps could contain intensity, local edge strength, range, or another quantity. All these maps, including the edge map, contain continuous rather than all-or-nothing information, following a strategy of least commitment. Each of the approaches is developed and tested on a parallel transputer network.>

This paper introduces a number of systems of coupled, non-linear diffusion equations and investigates their role in edge-preserving smoothing and noise suppression. The basic idea is that several maps describing the image undergo coupled development towards an equilibrium state representing the enhanced image. These maps could contain: e.g., intensity, local edge strength, range or another quantity. All these maps, including the edge map, contain continuous rather than all-or-nothing information, following a strategy of least commitment. Each of the approaches has been developed and tested on a parallel transputer network.

It is shown how object recognition and optical flow can be captured within a single framework. These examples have been selected because they illustrate two complementary problems which can be tackled using the same unified approach based on Lie theory. The object recognition work referred to is based on the extraction of shape invariants and has been reported elsewhere. The present study focuses on using the same framework for the calculation of the optical flow. Besides the introduction of some new methods, it is shown that several well-known schemes can be derived following the same principles.>