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Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.


Papers
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Journal ArticleDOI
01 Mar 1976
TL;DR: A method of detecting ``noisy'' branches of a skeleton, with the aid of the distance transform of the original object, is developed.
Abstract: Implementation of a simple algorithm is described for thinning digital objects down to ``skeletons.'' A method of detecting ``noisy'' branches of a skeleton, with the aid of the distance transform of the original object, is developed.

59 citations

Proceedings ArticleDOI
01 Oct 1997
TL;DR: To enable fast and efficient calculations, the concept of a distance map is introduced and two hierarchical collision detection algorithms are developed, taking advantage of the uniform structure of volumetric datasets.
Abstract: We propose a probability model for the handling of complicated interactions between volumetric objects. In our model each volume is associated with a "probability map" that assigns a "surface crossing" probability to each space point according to local volume properties. The interaction between two volumes is then described by finding the intersecting regions between the volumes, and calculating the "collision probabilities" at each intersecting point from the surface crossing probabilities. To enable fast and efficient calculations, we introduce the concept of a distance map and develop two hierarchical collision detection algorithms, taking advantage of the uniform structure of volumetric datasets.

59 citations

Journal ArticleDOI
TL;DR: A transformation invariant metric recently proposed in the machine learning literature to measure the distance between image manifolds - the tangent distance (TD) - is analyzed and shows that it is closely related to alignment techniques from the motion analysis literature.
Abstract: Accounting for spatial image transformations is a requirement for multimedia problems such as video classification and retrieval, face/object recognition or the creation of image mosaics from video sequences. We analyze a transformation invariant metric recently proposed in the machine learning literature to measure the distance between image manifolds - the tangent distance (TD) - and show that it is closely related to alignment techniques from the motion analysis literature. Exposing these relationships results in benefits for the two domains. On one hand, it allows leveraging on the knowledge acquired in the alignment literature to build better classifiers. On the other, it provides a new interpretation of alignment techniques as one component of a decomposition that has interesting properties for the classification of video. In particular, we embed the TD into a multiresolution framework that makes it significantly less prone to local minima. The new metric - multiresolution tangent distance (MRTD) - can be easily combined with robust estimation procedures, and exhibits significantly higher invariance to image transformations than the TD and the Euclidean distance (ED). For classification, this translates into significant improvements in face recognition accuracy. For video characterization, it leads to a decomposition of image dissimilarity into "differences due to camera motion" plus "differences due to scene activity" that is useful for classification. Experimental results on a movie database indicate that the distance could be used as a basis for the extraction of semantic primitives such as action and romance.

58 citations

Proceedings ArticleDOI
15 Mar 1999
TL;DR: This work proposes a new signed or unsigned Euclidean distance transformation algorithm, based on the local corrections of the well-known 4SED algorithm of Danielsson (1980), which produces perfect Euclideans distance maps in a time linearly proportional to the number of pixels in the image.
Abstract: We propose a new signed or unsigned Euclidean distance transformation algorithm, based on the local corrections of the well-known 4SED algorithm of Danielsson (1980). Those corrections are only applied to a small neighborhood of a small subset of pixels from the image, which keeps the cost of the operation low. In contrast with all fast algorithms previously published, our algorithm produces perfect Euclidean distance maps in a time linearly proportional to the number of pixels in the image. The computational cost is close to the cost of the 4SSED approximation.

58 citations

Journal ArticleDOI
TL;DR: This work presents a graphics hardware implementation of the tangent-plane algorithm for computing the kth-order Voronoi diagram of a set of point sites in image space, and describes the implementation in OpenGL and Cg, and several optimizations.
Abstract: We present a graphics hardware implementation of the tangent-plane algorithm for computing the kth-order Voronoi diagram of a set of point sites in image space. Correct and efficient implementation of this algorithm using graphics hardware is possible only with the use of an appropriate shader program on the GPU. This is achieved by rendering in k passes the parallel projection of the top k levels of an arrangement of planes tangent to the paraboloid z = x2+y2. Each level of the arrangement corresponds to the so-called kth-nearest point diagram, which is interesting in its own right. Composition of the images of the k top levels results in the kth-order Voronoi diagram. The diagram facilitates efficient computation of the k nearest neighbors of an arbitrary query point. We describe our implementation of the algorithm in OpenGL and Cg, and several optimizations. We also show how to efficiently compute the distance transform of the given sites using the GPU, based on the first-order Voronoi diagram.

58 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20235
202217
202161
202099
2019112
201881