Euclidean Distance Geometry and Applications

Liberti, Leo; Lavor, Carlile; Maculan, Nelson; Mucherino, Antonio

doi:10.1137/120875909

Cited by 382 publications

(328 citation statements)

References 195 publications

(305 reference statements)

Supporting

Mentioning

318

Contrasting

Unclassified

Order By: Relevance

“…The inputs required are an image, an object 314 mask, and a minimum distance to separate object peaks. The function uses the input mask to 315 calculate a Euclidean distance map (Liberti et al, 2014). Marker peaks calculated from the 316 distance map that meet the minimum distance setting are used in a watershed segmentation 317 algorithm (van der Walt et al, 2014) to segment and count the objects.…”

Section: Multi-plant Detection 249mentioning

confidence: 99%

PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

Gehan

Abbasi

Berry

et al. 2017

Preprint

View full text Add to dashboard Cite

32Systems for collecting image data in conjunction with computer vision techniques are a powerful 33 tool for increasing the temporal resolution at which plant phenotypes can be measured non-34 destructively. Computational tools that are flexible and extendable are needed to address the 35 diversity of plant phenotyping problems. We previously described the Plant Computer Vision 36 (PlantCV) software package, which is an image processing toolkit for plant phenotyping 37

show abstract

Section: Multi-plant Detection 249mentioning

confidence: 99%

PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

Gehan

Abbasi

Berry

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…We would like to thank one of the referees for bringing the references [5,6] to our attention. We also thank the associate editor for the useful comments that have helped to revise the paper.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…The sensor network localization (SNL) problem is to recover the sensor positions through those distances. The most popular method may be the classical Multidimensional Scaling (cMDS), well documented in [3,4], and a recent survey [5] and tutorial [6]. It works well when a large number of d ij are close to their true distances (i.e., ij s are relatively small).…”

Section: Introductionmentioning

confidence: 99%

Tackling the flip ambiguity in wireless sensor network localization and beyond

Bai

2016

Digital Signal Processing

View full text Add to dashboard Cite

There have been significant advances in range-based numerical methods for sensor network localizations over the past decade. However, there remain a few challenges to be resolved to satisfaction. Those issues include, for example, the flip ambiguity, high level of noises in distance measurements, and irregular topology of the concerning network. Each or a combination of them often severely degrades the otherwise good performance of existing methods. Integrating the connectivity constraints is an effective way to deal with those issues. However, there are too many of such constraints, especially in a large and sparse network. This presents a challenging computational problem to existing methods. In this paper, we propose a convex optimization model based on the Euclidean Distance Matrix (EDM). In our model, the connectivity constraints can be simply represented as lower and upper bounds on the elements of EDM, resulting in a standard 3-block quadratic conic programming, which can be efficiently solved by a recently proposed 3-block alternating direction method of multipliers. Numerical experiments show that the EDM model effectively eliminates the flip ambiguity and retains robustness in terms of being resistance to irregular wireless sensor network topology and high noise levels.

show abstract

“…Geralmente formulado como um problema de otimização global contínua [12], ele possui vários métodos de resolução por aproximação [11].…”

Section: Introductionunclassified

“…Além dessas duas representações, cada instância G = (V, S, d) do MDGP pode ser associada a uma matriz simétrica D G chamada de Matriz euclidiana de Distâncias [11], onde…”

Section: Introductionunclassified

Uma nova abordagem para dividir instâncias do Discretizable Molecular Distance Geomety Problem usando gaps

Fidalgo

Rodriguez

2015

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

Self Cite

View full text Add to dashboard Cite

Resumo: Motivado pelo Parallel BP, que divide instâncias do Discretizable Molecular Distance Geometry Problem (DMDGP) em sub-instâncias com o mesmo número de vértices cada, este trabalho propõe duas divisões mais flexíveis, baseadas nas informações de distâncias disponíveis. Tais abordagens foram desenvolvidas na tentativa de separar as partes com rápida determinação das partes mais lentas, em relação ao custo computacional do Algoritmo Branch & Prune, utilizando os chamados gaps. Resultados compuacionais preliminares indicam que abordagemé mais eficiente. IntroduçãoUnindo certo conhecimento químico, como comprimentos eângulos de ligações entreátomos, com dados de Ressonância Magnética Nuclear (RMN) [5,16], pode-se estimar distâncias entre pares deátomos em moléculas. Tais informações induzem a formulação de um problema inverso para determinar conformações tridimensionais, preservando suas estruturas deângulos e distâncias. Esteé chamado de Molecular Distance Geometry Problem (MDGP) [1]. Definição 1.1 (MDGP [8]). Uma molécula M , com nátomos, associa-se a um grafo G = (V, S, d) não-direcionado e ponderado, cujos vértices em V correspondem aosátomos, as arestas em S correspondemàs ligações e os pesos das arestas, dados pela função d : V −→ R, associamseàs distâncias disponíveis entre osátomos.É possível determinar uma conformação de M x : V −→ R 3 tal queCada conformação x recebe o nome de Realização de G e, por Saxe [15], este problemaé NP-difícil. Geralmente formulado como um problema de otimização global contínua [12], ele possui vários métodos de resolução por aproximação [11].Além dessas duas representações, cada instância G = (V, S, d) do MDGP pode ser associada a uma matriz simétrica D G chamada de Matriz euclidiana de Distâncias [11], onde

show abstract

Euclidean Distance Geometry and Applications

Cited by 382 publications

References 195 publications

PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

PlantCV v2.0: Image analysis software for high-throughput plant phenotyping

Tackling the flip ambiguity in wireless sensor network localization and beyond

Uma nova abordagem para dividir instâncias do Discretizable Molecular Distance Geomety Problem usando gaps

Contact Info

Product

Resources

About