An efficient uniform cost algorithm applied to distance transforms

Verwer, Ben J. H.; Verbeek, P.W.; Dekker, S.T.

doi:10.1109/34.19041

Cited by 134 publications

(72 citation statements)

References 7 publications

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“…As shown by Verwer [8] for constrained chamfer DT and Ragnemalm [14] for Euclidean DT, this can be accomplished by bucket sorting the pixels in the propagation front. Instead of using a single list, pixels to be propagated are stored in a number of buckets, one for each possible distance value.…”

Section: Propagation Dt Using Bucket Sortingmentioning

confidence: 99%

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Cuisenaire

Macq

1999

Computer Vision and Image Understanding

127

108

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We propose a new exact Euclidean distance transformation (DT) by propagation, using bucket sorting. A fast but approximate DT is first computed using a coarse neighborhood. A sequence of larger neighborhoods is then used to gradually improve this approximation. Computations are kept short by restricting the use of these large neighborhoods to the tile borders in the Voronoi diagram of the image. We assess the computational cost of this new algorithm and show that it is both smaller and less image-dependent than all other DTs recently proposed. Contrary to all other propagation DTs, it appears to remain o(n 2 ) even in the worst-case scenario.

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Section: Propagation Dt Using Bucket Sortingmentioning

confidence: 99%

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Cuisenaire

Macq

1999

Computer Vision and Image Understanding

127

108

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“…The Grey Weighted Distance Transform (GWDT) method can group and complete such truncated tracks to form re-annealed trajectories using probabilistic criteria. 53 The final segmented pseudo-3D curve is linked by pairs of points from the tail of one trajectory to the head of another trajectory.…”

Section: Pinpointing Fluorescently-labelled Moleculesmentioning

confidence: 99%

Analytical tools for single-molecule fluorescence imaging in cellulo

Leake

2014

Phys. Chem. Chem. Phys.

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Recent technological advances in cutting-edge ultrasensitive fluorescence microscopy have allowed single-molecule imaging experiments in living cells across all three domains of life to become commonplace. Single-molecule live-cell data is typically obtained in a low signal-to-noise ratio (SNR) regime sometimes only marginally in excess of 1, in which a combination of detector shot noise, suboptimal probe photophysics, native cell autofluorescence and intrinsically underlying stochasticity of molecules result in highly noisy datasets for which underlying true molecular behaviour is nontrivial to discern. The ability to elucidate real molecular phenomena is essential in relating experimental single-molecule observations to both the biological system under study as well as offering insight into the fine details of the physical and chemical environments of the living cell. To confront this problem of faithful signal extraction and analysis in a noise-dominated regime, the 'needle in a haystack' challenge, such experiments benefit enormously from a suite of objective, automated, high-throughput analysis tools that can home in on the underlying 'molecular signature' and generate meaningful statistics across a large population of individual cells and molecules. Here, I discuss the development and application of several analytical methods applied to real case studies, including objective methods of segmenting cellular images from light microscopy data, tools to robustly localize and track single fluorescently-labelled molecules, algorithms to objectively interpret molecular mobility, analysis protocols to reliably estimate molecular stoichiometry and turnover, and methods to objectively render distributions of molecular parameters.

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“…Section 2 is dedicated to the notion of the grey-weighted distance which is the core of one of the algorithms that we propose for the contour valuation. We introduce a new tensor for its computation over discrete chamfer metrics and we brie#y note a modi"cation for the grey-weighted distance transform implementation in [28]. Section 3 provides a detailed description of the proposed hierarchical segmentation scheme.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the fact that at a regional minimum, either as a source or a destination region, the algorithm has to attribute the same distance to all the pixels which belong to the isovalued connected component. Therefore, we have modi"ed the GWDT algorithm as it has been proposed in [28], to treat the source and destination reference sets appropriately. An algorithmic description can be found in [19].…”

Section: Introductionmentioning

confidence: 99%

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Low level image partitioning guided by the gradient watershed hierarchy

1999

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In this paper, we study the construction of hierarchies for gradient watersheds. The underlying analysis is based on the fact that the gradient image provides information with respect to local discontinuity and similarity. Two algorithms which utilize this type of information are analyzed. The "rst one is an extended version of the dynamics of contours and the second is the algorithm which considers the minimum cost path saliency between two adjacent regional minima. For both algorithms, a stopping criterion which de"nes and extracts automatically each hierarchical level is proposed. An important hint to determine which one of the two algorithms has a superior behavior is the domination of the pro"le type of the surface (convex or concave) formed by the gradient image. Tests are discussed on arti"cially generated and real images. The proposed bottom-up procedure provides an essential aid to the precise classi"cation of anatomical objects when used together with a user-interface environment.1999 Elsevier Science B.V. All rights reserved. ZusammenfassungIm folgenden Beitrag werden wir die Konstruktion von Hierarchien fuK r Wendepunkte untersuchen. Die zugrundeliegende Analyse beruht auf da{ Gradientenbilder Informationen uK ber lokale DiskontinuitaK ten und A G hnlichkeiten beinhalten. Es werden zwei Algorithmen untersucht, die diese Art von Information nutzen. Der erste ist eine erweiterte Version der Dynamik von Konturen und der zweite Algorithmus betrachtet minimale SpruK nge der Kostenfunktion zwischen zwei benachbarten regionalen Minima. FuK r beide Algorithmen wird ein Abbruchkriterium vorgestellt, das automatisch jede hierarchische Stufe de"niert und extrahiert. Ein wichtiger Hinweis dafuK r, welcher der beiden Algorithmen ein besseres Verhalten liefert, ist die Dominanz des Pro"ltyps der Ober#aK che (konvex oder konkav), gegeben durch das Gradientenbild. Tests mit kuK nstlichen und realen Bildern werden diskutiert. Die vorgestellte bottom-up Methode liefert eine entscheidende Hilfe zur praK zisen Klassi"zierung anatomischer Objekte, wenn sie im Zusammenhang mit einer Benutzerschnittstelle eingesetzt wird.1999 Elsevier Science B.V. All rights reserved. Re2 sume2Dans cet article, nous eH tudions la construction de hieH rarchies pour les lignes de partage des eaux du gradient. L'analyse sous-jacente repose sur le fait que l'image gradient fournit de l'information relative aux discontinuiteH s et aux similariteH s 0165-1684/99/$ -see front matter 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 5 -1 6 8 4 ( 9 8 ) 0 0 2 3 2 -1 locales. Deux algorithmes qui utilisent ce type d'information sont analyseH s. Le premier est une version eH tendue de la dynamique des contours et le deuxie`me est un algorithme qui conside`re le chemin de cou( t minimum entre deux minima reH gionaux adjacents. Pour les deux algorithmes, nous proposons un crite`re d'arre( t qui deH "nit et extrait automatiquement chaque niveau hieH rarchique. Une indication importante pour deH terminer lequel des deux algorithmes a un...

show abstract

An efficient uniform cost algorithm applied to distance transforms

Cited by 134 publications

References 7 publications

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Analytical tools for single-molecule fluorescence imaging in cellulo

Low level image partitioning guided by the gradient watershed hierarchy

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