Fast exact Euclidean distance (FEED) transformation

Schouten, Theo E.; Broek, Egon van den

doi:10.1109/icpr.2004.1334599

Cited by 18 publications

(42 citation statements)

References 5 publications

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“…where (x 1 , y 1 ) and (x 2 , y 2 ) ∈ Z , FEED [12,20], and Lucent's LLT [68]. This shows that the execution time is dependent on the content of de images; for example, the percentage of object pixels, as shown here, but also the border pixels.…”

Section: On More Than 30 Years Of Researchmentioning

confidence: 84%

“…Fig. (2) presents some DIs of a single pixel (in the center of the image; look closely) as well as their deviation from the ED [1,2,12,[20][21][22][23][24]. So, as is also illustrated in both Fig.…”

Section: Introductionmentioning

confidence: 86%

“…Table 2 provides the error statistics for the images visualized here. Note that the (EDI (# C1) is also the output of Maurer et al [33], FEED [12,20], and Lucent's LLT [68]; see also where x and y are n-tuples, i is used to denote their n coordinates (or dimensions), and…”

Section: Distance Transformmentioning

confidence: 99%

“…In the same year as FEED was launched [20], Shih and Wu [24] introduced their two scan method, with which they claimed to be able to obtain true exact EDTs. However, their algorithm does not do so in all cases (cf.…”

Section: The Last Decadementioning

confidence: 99%

“…Table 1 provides the error statistics for the images visualized here. Note that the (EDI (# C1) is also the output of Danielsson [17], Maurer et al [33], FEED [12,20], and Lucent's LLT [68]; see also Table 1. Further, note that to optimize the percept of the visualizations, the original intensity values i of all the images shown here {Ix} were transformed as follows: 255(i − min Ix )/ max Ix − min Ix .…”

Section: Introductionmentioning

confidence: 99%

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Distance Transforms: Academics Versus Industry

Broek

Schouten²

2011

CSENG

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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.

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Section: On More Than 30 Years Of Researchmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 86%

Section: Distance Transformmentioning

confidence: 99%

Section: The Last Decadementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distance Transforms: Academics Versus Industry

Broek

Schouten²

2011

CSENG

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Euclidean Distance Transform of Digital Images in Arbitrary Dimensions

2006

Advances in Multimedia Information Processing - PCM 2006

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Abstract.A new algorithm for Euclidean distance transform is proposed in this paper. It propagates from the boundary to the inner of object layer by layer, like the inverse propagation of water wave. It can be applied in every dimensional space and has linear time complexity. Euclidean distance transformations of digital images in 2-D and 3-D are conducted in the experiments. Voronoi diagram and Delaunay triangulation can also be produced by this method.

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Distance Transformation on Two-Dimensional Irregular Isothetic Grids

Vacavant

Cœurjolly

Tougne

Discrete Geometry for Computer Imagery

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In this article, we propose to investigate the extension of the E 2 DT (squared Euclidean Distance Transformation) on irregular isothetic grids. We give two algorithms to handle different structurations of grids. We first describe a simple approach based on the complete Voronoi diagram of the background irregular cells. Naturally, this is a fast approach on sparse and chaotic grids. Then, we extend the separable algorithm defined on square regular grids proposed in [22], more convenient for dense grids. Those two methodologies permit to process efficiently E 2 DT on every irregular isothetic grids. The authors would like to thank Pr. Annick Montanvert for her constructive suggestions during the preparation of this article.

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Fast exact Euclidean distance (FEED) transformation

Cited by 18 publications

References 5 publications

Distance Transforms: Academics Versus Industry

Distance Transforms: Academics Versus Industry

Euclidean Distance Transform of Digital Images in Arbitrary Dimensions

Distance Transformation on Two-Dimensional Irregular Isothetic Grids

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