Separable algorithms for distance transformations on irregular grids

Vacavant, Antoine; Cœurjolly, David; Tougne, Laure

doi:10.1016/j.patrec.2010.11.010

Cited by 4 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another study investigates models for imprecision in the precise location of digital objects [54]. A different but related aspect to this, concerning distance transforms defined on irregular grids, was explored in [56][57][58]. The SDT proposed in this work also belongs to category (i).…”

Section: Distance Transformsmentioning

confidence: 99%

Stochastic Distance Transform: Theory, Algorithms and Applications

2020

View full text Add to dashboard Cite

Distance transforms (DTs) are standard tools in image analysis, with applications in image registration and segmentation. The DT is based on extremal (minimal) distance values and is therefore highly sensitive to noise. We present a stochastic distance transform (SDT) based on discrete random sets, in which a model of element-wise probability is utilized and the SDT is computed as the first moment of the distance distribution to the random set. We present two methods for computing the SDT and analyze them w.r.t. accuracy and complexity. Further, we propose a method, utilizing kernel density estimation, for estimating probability functions and associated random sets to use with the SDT. We evaluate the accuracy of the SDT and the proposed framework on images of thin line structures and disks corrupted by salt and pepper noise and observe excellent performance. We also insert the SDT into a segmentation framework and apply it to overlapping objects, where it provides substantially improved performance over previous methods. Finally, we evaluate the SDT and observe very good performance, on simulated images from localization microscopy, a state-of-the-art super-resolution microscopy technique which yields highly spatially localized but noisy point-clouds.

show abstract

Section: Distance Transformsmentioning

confidence: 99%

Stochastic Distance Transform: Theory, Algorithms and Applications

2020

View full text Add to dashboard Cite

show abstract

“…ample, triangular and hexagonal grids [48][49][50] and sparse grids [51]. For an overview of the possible grids, we refer to [5].…”

Section: Distance Transformmentioning

confidence: 99%

Distance Transforms: Academics Versus Industry

Broek

Schouten²

2011

CSENG

View full text Add to dashboard Cite

Abstract:In image and video analysis, distance transformations (DT) are frequently used. They provide a distance image (DI) of background pixels to the nearest object pixel. DT touches upon the core of many applications; consequently, not only science but also industry has conducted a significant body of work in this field. However, in a vast majority of the cases this has not been published in major scientific outlets but has been filed as a patent application. This article provides a brief introduction into DT, including a specification of a few of the most prominent algorithms in the field. Next, a few interesting algorithms from the last decade are discussed. A benchmark including eight DT algorithms (i.e., city block, Danielsson's algorithm, chamfer 3-4, hexadecagonal region growing, a recent claimed true Euclidean DT, and three exact Euclidean DT) has been executed, which illustrates the intriguing complexity of DT in terms of precision and computational complexity. Subsequently, a selection of key patent applications are discussed that have emerged in this field, including their scientific merit and areas of application. Finally, this article's findings are summarized and discussed, with an emphasis on both the common ground of scientific articles and patent applications as well as the added value they can have to each other.

show abstract

Separable Distance Transformation and Its Applications

Cœurjolly

Vacavant

2012

Digital Geometry Algorithms

View full text Add to dashboard Cite

Separable algorithms for distance transformations on irregular grids

Cited by 4 publications

References 20 publications

Stochastic Distance Transform: Theory, Algorithms and Applications

Stochastic Distance Transform: Theory, Algorithms and Applications

Distance Transforms: Academics Versus Industry

Separable Distance Transformation and Its Applications

Contact Info

Product

Resources

About