Topic
Distance transform
About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.
Papers published on a yearly basis
Papers
More filters
23 Jun 1993
TL;DR: An algorithm for computing the Euclidean distance from the boundary of a given digitized shape is presented and the distance is calculated with sub-pixel accuracy.
Abstract: An algorithm for computing the Euclidean distance from the boundary of a given digitized shape is presented. The distance is calculated with sub-pixel accuracy. The algorithm is based on an equal distance contour evolution process. The moving contour is embedded as a level set in a time varying function of higher dimension. This representation of the evolving contour makes possible the use of an accurate and stable numerical scheme, due to Osher and Sethian.
21 citations
19 Nov 2003
TL;DR: In this paper, the authors proposed an algorithm which computes the look-up table and the neighbourhood to be tested in the case of chamfer distances, and showed that results have completely different properties.
Abstract: Medial Axis (MA), also known as Centres of Maximal Disks, is a useful representation of a shape for image description and analysis. MA can be computed on a distance transform, where each point is labelled to its distance to the background. Recent algorithms allow to compute Squared Euclidean Distance Transform (SEDT) in linear time in any dimension. While these algorithms provide exact measures, the only known method to characterize MA on SEDT, using local tests and Look-Up Tables, is limited to 2D and small distance values [5]. We have proposed in [14] an algorithm which computes the look-up table and the neighbourhood to be tested in the case of chamfer distances. In this paper, we adapt our algorithm for SEDT in arbitrary dimension and show that results have completely different properties.
21 citations
19 Nov 2003
TL;DR: In this article, a novel approach to normalize binary shapes based on the Radon transform is presented. But this approach is limited to 3-4-level distance transform and the accuracy and efficiency of the proposed algorithm in the presence of a variety of transformations is demonstrated within a shape recognition process.
Abstract: This paper presents a novel approach to normalize binary shapes which is based on the Radon transform. The key idea of the paper is an original adaptation of the Radon transform. The binary shape is projected in Radon space for different levels of the (3-4) distance transform. This decomposition gives rise to a representation which has a nice behavior with respect to common geometrical transformations. The accuracy and the efficiency of the proposed algorithm in the presence of a variety of transformations is demonstrated within a shape recognition process.
21 citations
09 Jun 2004
TL;DR: This paper combines morphing with deformation theory from continuum mechanics by using strain energy, which reflects the magnitude of deformation, as an objective function, to convert the problem of path interpolation into an unconstrained optimization problem.
Abstract: When two topologically identical shapes are blended, various possible transformation paths exist from the source shape to the target shape. Which one is the most plausible? Here we propose that the transformation process should obey a quasi-physical law. This paper combines morphing with deformation theory from continuum mechanics. By using strain energy, which reflects the magnitude of deformation, as an objective function, we convert the problem of path interpolation into an unconstrained optimization problem. To reduce the number of variables in the optimization we adopt shape functions, as used in the finite element method (FEM). A point-to-point correspondence between the source and target shapes is naturally established using these polynomial functions plus a distance map.
21 citations
10 Dec 2002
TL;DR: It is found that the cosine angle distance, in general, works equally well for image databases and shows, for a given query vector, the characteristics of feature vectors that will be favored by one measure but not by the other.
Abstract: The Euclidean distance measure has been used in comparing feature vectors of images, while the cosine angle distance measure is used in document retrieval. We theoretically analyze these two distance measures based on feature vectors normalized by image size and experiment with them in the context of a color image database. We find that the cosine angle distance, in general, works equally well for image databases. We show, for a given query vector, the characteristics of feature vectors that will be favored by one measure but not by the other. We compute k-nearest neighbors for query images using both Euclidean and cosine angle distance for a small image database. The experimental data corroborate our theoretical results.
21 citations