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About 1,106 papers for “Fast marching method”

A multilevel fast-marching method

Marianne Akian, Stéphane Gaubert, Shanqing Liu · HAL (Le Centre pour la Communication Scientifique Directe) · 2022

International audience

Cited by 0Open access

Video Segmentation Using Fast Marching Method

Piotr Stec, Marek Domanski · 2002

The paper deals with a novel segmentation technique applicable to colour video sequences. The algorithm uses Fast Marching Method for automatic extraction of semantic objects from natural colour video sequences by joint motion and colour analysis. The algorithm handles background in the same way as other objects, thus it does not need global motion compensation. The number of control parameters is small.

Cited by 0

Optimal control using the Fast Marching Method

Santiago Garrido, Luís Moreno, Dolores Blanco et al. · 2009

This paper presents the applications of the Fast Marching and the Buffered Fast Marching Methods to solve typical control problems. Calculus of Variations and Optimal Control problems can be solved using the Euler-Lagrange or the Pontryagin equations and solving analytically or numerically the corresponding differential equations systems. The proposed method solves directly these control problems, calculating directly the control signal. Fast Marching methods applied to control have a very important advantage: all kind of restrictions can be considered in a direct and easy way.

Cited by 0

Seismic, Migration, Fast Marching Method, Priority Queue

Edwin Mauricio Vargas Diaz · Universidad Industrial de Santander · 2016

Las imágenes sísmicas son una representación del interior de la tierra que permiten identificar objetivos de exploración en la industria del petróleo, generalmente, estas imágenes se representan en dos sistemas de coordenadas: coordenadas de tiempo y profundidad. Las imágenes en tiempo son adecuadas para áreas donde la velocidad sísmica depende principalmente de la profundidad y las imágenes en profundidad para áreas estructuralmente complejas o de velocidad sísmicas arbitraria. Este trabajo se basa en un algoritmo de conversión tiempo a profundidad propuesto por Maria Kourkina Cameron que tiene como objetivo convertir imágenes sísmicas representadas en coordenadas de tiempo a coordenadas de profundidad; el principal bloque de construcción de este algoritmo de conversión es el método de marcha rápida propuesto por James Sethian. El objetivo de este trabajo es implementar y evaluar el desempeño del algoritmo de conversión tiempo a profundidad construido con una eficiente versión del método de marcha rápida basada en una estructura de datos denominada pila de prioridad desordenada. Para la evaluación, se tienen en cuenta dos criterios, tiempo de ejecución del algoritmo y precisión de la imagen obtenida. Los resultados demuestran que el algoritmo de conversión implementado con el método de marcha rápida desordenada mejora el tiempo de ejecución con una perdida despreciable de precisión de la imagen resultante. 1

Cited by 0Open access

Marine Applications of the Fast Marching Method

Santiago Garrido, David Álvarez, Luís Moreno · Frontiers in Robotics and AI · 2020

Path planning is general problem of mobile robots, which has special characteristics when applied to marine applications. In addition to avoid colliding with obstacles, in marine scenarios, environment conditions such as water currents or wind need to be taken into account in the path planning process. In this paper, several solutions based on the Fast Marching Method are proposed. The basic method focus on collision avoidance and optimal planning and, later on, using the same underlying method, the influence of marine currents in the optimal path planning is detailed. Finally, the application of these methods to consider marine robot formations is presented.

Cited by 26Open access

Linear sorting method for the traveltime fast marching method

Ningcheng Cui, Guangnan Huang · 2019

Fast marching method is widely used in seismic traveltime tomography and Kirchhoff per-stack depth migration, which has the advantages of high computational efficiency, unconditional stability and satisfactory accuracy. According to the propagation law of the seismic wavefield, this finite-difference algorithm divides the traveltime points into alive points, close points and far points for iterative calculation.

Cited by 0

Integrating Surface Normal Vectors Using Fast Marching Method

Jeffrey Ho, Jongwoo Lim, Ming-hsuan Yang et al. · 2006

Abstract. Integration of surface normal vectors is a vital component in many shape reconstruction algorithms that require integrating surface normals to produce their final outputs, the depth values. In this paper, we introduce a fast and efficient method for computing the depth val-ues from surface normal vectors. The method is based on solving the Eikonal equation using Fast Marching Method. We introduce two ideas. First, while it is not possible to solve for the depths Z directly using Fast Marching Method, we solve the Eikonal equation for a function W of the form W = Z+λf. With appropriately chosen values for λ, we can ensure that the Eikonal equation for W can be solved using Fast March-ing Method. Second, we solve for W in two stages with two different λ values, first in a small neighborhood of the given initial point with large λ, and then for the rest of the domain with a smaller λ. This step is needed because of the finite machine precision and rounding-off errors. The proposed method is very easy to implement, and we demonstrate experimentally that, with insignificant loss in precision, our method is considerably faster than the usual optimization method that uses conju-gate gradient to minimize an error function. 1

Cited by 0

A Fast Marching Method For 3D Reservoir Simulation

K.H. Karlsen, K.-A. Lie, K. {a. Lie et al. · 2000

this paper, we assume that Pn = Pw = P , thereby neglecting capillary pressure. Furthermore, we assume incompressible medium and uids. Conservation of mass for each uid phase reads

Cited by 0

Comparison principle for a generalized fast marching method

Nicolas Forcadel · HAL (Le Centre pour la Communication Scientifique Directe) · 2015

In [4], the authors have proposed a generalization of the classical Fast Marching Method of Sethian for the eikonal equation in the case where the normal velocity depends on space and time and can change sign. The goal of this paper is to propose a modified version of the Generalized Fast Marching Method proposed in [4] for which we state a general comparison principle. We also prove the convergence of the new algorithm. AMS Classification: 65M06, 65M12, 49L25.

Cited by 0Open access

Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions

Lucas Janson, Edward Schmerling, Ashley Clark et al. · The International Journal of Robotics Research · 2015

In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM* and RRT*. The FMT* algorithm performs a "lazy" dynamic programming recursion on a predetermined number of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-arrive space. As such, this algorithm combines features of both single-query algorithms (chiefly RRT) and multiple-query algorithms (chiefly PRM), and is reminiscent of the Fast Marching Method for the solution of Eikonal equations. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT* algorithm is analyzed under the notion of convergence in probability: the extra mathematical flexibility of this approach allows for convergence rate bounds-the first in the field of optimal sampling-based motion planning. Specifically, for a certain selection of tuning parameters and configuration spaces, we obtain a convergence rate bound of order <i>O</i>(<i>n</i><sup>-1/<i>d</i>+ρ</sup>), where <i>n</i> is the number of sampled points, <i>d</i> is the dimension of the configuration space, and ρ is an arbitrarily small constant. We go on to demonstrate asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Numerical experiments over a range of dimensions and obstacle configurations confirm our the-oretical and heuristic arguments by showing that FMT*, for a given execution time, returns substantially better solutions than either PRM* or RRT*, especially in high-dimensional configuration spaces and in scenarios where collision-checking is expensive.

Cited by 563Open access

Universal Fast Marching Method to Identify Liver Image

Sangeeta K. Siri, Pramod Kumar S, Gavisiddappa · IOP Conference Series Materials Science and Engineering · 2020

Abstract Liver segmentation is of prime importance in modern liver disease diagnosis and analysis. In our paper, random section of liver image is chosen and its histogram is achieved. From histogram, liver pixel intensity range is obtained. Using the range value, threshold segmentation is carried out which detaches the liver from its adjoining organs. Median filter is employed to curtail the noise. The sigmoidal function is applied to improve anatomical structures of image. Then the image is converted into binary called as speed function. The novel algorithm is designed to locate the start points within speed function without user intervention. These start point evolved outwardly using Fast Marching Method till complete periphery of liver is reached. The proposed algorithm is compared with popularly used segmentation algorithms. The results show that proposed segmentation algorithm is robust in approach.

Cited by 1Open access

Augmented Fast Marching Method (AFMM) Image Morphological Skeletonization

P. Manjunatha · Zenodo (CERN European Organization for Nuclear Research) · 2026

This package implements an augmented fast marching method algorithm for the skeletonization of binary masks in C, Python, Cython, and Pybind, as provided by Alexandru C. Telea et al. in their work "An augmented Fast Marching Method for computing skeletons and centerlines and Tolerance-Based Feature Transforms." Full Changelog: https://github.com/preethamam/AFMM-Image-Morphological-Skeletonization/compare/1.0.0...1.0.1

Cited by 0Open access

Augmented Fast Marching Method (AFMM) Image Morphological Skeletonization

P. Manjunatha · Zenodo (CERN European Organization for Nuclear Research) · 2026

This package implements an augmented fast marching method algorithm for the skeletonization of binary masks in C, Python, Cython, and Pybind, as provided by Alexandru C. Telea et al. in their work "An augmented Fast Marching Method for computing skeletons and centerlines and Tolerance-Based Feature Transforms." Full Changelog: https://github.com/preethamam/AFMM-Image-Morphological-Skeletonization/compare/1.0.0...1.0.1

Cited by 0Open access

M.: Fast marching trees: a fast marching sampling-based method for optimal motion planning in many dimensions

Lucas Janson, Ashley Clark, Marco Pavone · 2013

In this paper we present a novel probabilistic sampling-based motion planning algo-rithm called the Fast Marching Tree algorithm (FMT∗). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configura-tion spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM ∗ and RRT ∗. An additional advantage of FMT ∗ is that it builds and maintains paths in a tree structure (especially useful for planning under differential constraints). The FMT ∗ algorithm essentially performs a “lazy ” dynamic programming recursion on a set of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-come space. As such, this algorithm combines features of both single-query algorithms (chiefly RRT) and multiple-query algorithms (chiefly PRM), and is conceptually related to the Fast Marching Method for the solution of eikonal equations. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT ∗ algorithm is analyzed under the notion of ∗This work was originally presented at the 16th International Symposium on Robotics Research, ISRR 2013. This revised version includes an extended description of the FMT ∗ algorithm, proofs of all results, extended discussions about convergence rate and computational complexity, and a larger set of numerical experiments. 1 ar

Cited by 0

Recovering 3D Shape Using an Improved Fast Marching Method

Chengming Zou, Edwin R. Hancock · 2010

In this paper we present an improved shape from shading method using improved fast marching method. We commence by showing how to recover 3D shape from a single image using an improved fast marching method for solving SFS problem. Then we use the level set method constrained by energy minimization to evolve the 3D shape. Finally we show that the method can recover stable surface estimates from both synthetic and real world images of complex objects. The experimental results show that the resulting method is both robust and accurate.

Cited by 3