Editorial

Machine Vision and Applications -     

A syntactic approach to scale-space-based corner description

Planar curves are described by information about corners integrated over various levels of resolution. The detection of corners takes place on a digital representation. To compensate for ambiguities arising from sampling problems due to the discreteness, results about the local behavior of curvature extrema in continuous scale-space are employed     

On the search of optimal reconstruction resolution

In this paper we present a novel algorithm to optimize the reconstruction from non-uniform point sets. We introduce a statistically-derived topology-controller for selecting the reconstruction resolution of a given non-uniform point set. Deriving information from homology-based statistics, our topology-controller ensures a stable and sound basis for the analysis process. By analyzing our topology-controller, we select an optimal reconstruction resolution which ensures both low reconstruction errors and a topological stability of the underlying signal. Our approach offers a valuable method for the evaluation of the reconstruction process without the need of visual inspection of the reconstructed datasets. By means of qualitative results we show how our proposed topology statistics provides complementary information in the enhancement of existing reconstruction pipelines in visualization.     

A Minimal Line Property Preserving Representation of Line Images

In line image understanding a minimal line property preserving (MLPP) graph of the image compliments the structural information in geometric graph representations like the run graph. With such a graph and its dual it is possible to efficiently detect topological features like loops and holes and to make use of relations like containment. We present a new rule based method on dual graph contraction for transforming the run graph and its dual into MLPP graphs. A parallel O(log(longest curve)) algorithm is presented and results given. Received: May 28, 1998; revised November 17, 1998     

Topology-based image segmentation using LBP pyramids

In this paper, we present a new image segmentation algorithm which is based on local binary patterns (LBPs) and the combinatorial pyramid and which preserves structural correctness and image topology. For this purpose, we define a codification of LBPs using graph pyramids. Since the LBP code characterizes the topological category (local max, min, slope, saddle) of the gray level landscape around the center region, we use it to obtain a “minimal” image representation in terms of the topological characterization of a given 2D grayscale image. Based on this idea, we further describe our hierarchical texture aware image segmentation algorithm and compare its segmentation output and the “minimal” image representation.     

A pyramid that grows by powers of 2

The features of a new pyramid scheme are presented. It is based on a decomposition of a square grid into overlapping 2 × 2 blocks. The diagonal of the square cell becomes the side of a square at the next higher level. Assume unit length at the bottom level 0 of the pyramid, then the side length of a cell at level n is $\text{2}{}^{\mathrm{<mtext>n</mtext><mtext>2</mtext>}}$. The number of the elements at level n + 1 is one half of the level n.     

The generation of SAR layover and shadow maps from digitalelevation models

As almost fully automated techniques as well as expert systems obtain more and more importance in the geocoding of synthetic aperture radar (SAR) images, the solution to the problem of the identification of homologue points between the SAR image and reference system moves into the fields of pattern recognition and feature matching. Shadow regions, which appear as dark regions in the SAR image, are independent from the backscattering of the imaged terrain. Due to the multiplicity of signals, layover regions appear brighter than the surrounding regions. Hence, the phenomena of layover and shadow are stressed in the present paper. The reasons for their occurrence are studied through the digital elevation model representing the Earth's surface. An algorithm to define layover and shadow regions directly in the geometry of the digital elevation model is presented. The results are given in a so-called layover and shadow map     

Multi-scale 2D tracking of articulated objects using hierarchical spring systems

This paper presents a flexible framework to build a target-specific, part-based representation for arbitrary articulated or rigid objects. The aim is to successfully track the target object in 2D, through multiple scales and occlusions. This is realized by employing a hierarchical, iterative optimization process on the proposed representation of structure and appearance. Therefore, each rigid part of an object is described by a hierarchical spring system represented by an attributed graph pyramid. Hierarchical spring systems encode the spatial relationships of the features (attributes of the graph pyramid) describing the parts and enforce them by spring-like behavior during tracking. Articulation points connecting the parts of the object allow to transfer position information from reliable to ambiguous parts. Tracking is done in an iterative process by combining the hypotheses of simple trackers with the hypotheses extracted from the hierarchical spring systems.