Particle Swarm Optimization for Bézier Surface Reconstruction

Gálvez, Antonio; Cobo, Ángel; Puig-Pey, Jaime; Iglesias, Andrés

doi:10.1007/978-3-540-69387-1_13

Cited by 37 publications

(13 citation statements)

References 19 publications

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“…Genetic algorithms have been applied to this problem in both the discrete version (Sarfraz and Raza 2001) and the continuous version Yoshimoto et al 2003). Other metaheuristic approaches applied to this problem include the use of the popular particle swarm optimization technique (Gálvez et al 2008;Iglesias 2011, 2012), artificial immune systems (Gálvez et al 2013Gálvez and Iglesias 2016), firefly algorithm (Gálvez and Iglesias 2013b, c), estimation of distribution algorithms (Zhao et al 2011), memetic algorithms , and hybrid techniques (Gálvez and Iglesias 2013a), (Sarfraz and Raza 2001).…”

Section: Previous Workmentioning

confidence: 99%

Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Iglesias

Gálvez

2016

Nat Comput

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Surface reconstruction is a very important issue with outstanding applications in fields such as medical imaging (computer tomography, magnetic resonance), biomedical engineering (customized prosthesis and medical implants), computer-aided design and manufacturing (reverse engineering for the automotive, aerospace and shipbuilding industries), rapid prototyping (scale models of physical parts from CAD data), computer animation and film industry (motion capture, character modeling), archaeology (digital representation and storage of archaeological sites and assets), virtual/augmented reality, and many others. In this paper we address the surface reconstruction problem by using rational Bézier surfaces. This problem is by far more complex than the case for curves we solved in a previous paper. In addition, we deal with data points subjected to measurement noise and irregular sampling, replicating the usual conditions of real-world applications. Our method is based on a memetic approach combining a powerful metaheuristic method for global optimization (the electromagnetism algorithm) with a local search method. This method is applied to a benchmark of five illustrative examples exhibiting challenging features. Our experimental results show that the method performs very well, and it can recover the underlying shape of surfaces with very good accuracy.

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Section: Previous Workmentioning

confidence: 99%

Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Iglesias

Gálvez

2016

Nat Comput

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“…Applications to this domain include the EA in [33,34], where genetic algorithms were introduced to computer graphics. Later on, other evolutionary algorithms were also applied [14,2], while the most recent studies include also genetic algorithms [18] for polynomial Bspline surface reconstruction, particle swarm optimization [16] for the same problem and particle swarm optimization for Bézier surface reconstruction [15]. For example, Gálvez and Iglesias [17] applied the firefly algorithm [10,11] for polynomial Bézier surface parameterization, which is a well known problem in computer graphics.…”

Section: Introductionmentioning

confidence: 98%

Particle swarm optimization for automatic creation of complex graphic characters

Perc

Ljubič

Kamal

et al. 2015

Chaos, Solitons & Fractals

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“…Galvez et al [4] use PSO to perform surface fitting with a Bezier surface to a given cloud of data points. Delint et al [1] use PSO for curve fitting using NURBS.…”

Section: Introductionmentioning

confidence: 99%

Conic Curve Fitting Using Particle Swarm Optimization: Parameter Tuning

Yahya

Piah

Majid

2012

Communications in Computer and Information Science

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Abstract. We solve curve fitting problems using Particle Swarm Optimization (PSO). PSO is used to optimize control points and weights of two conic curves to a set of data points. PSO is used to find the best middle control point and weight for both conic curves to provide piecewise conics that preserve G^1 continuity. We present numerical result using parameter changes in PSO scheme. We obtain appropriate parameter values of PSO that provide best error and fastest time to solve curve fitting problem.Keywords: Curve fitting, particle swarm optimization, parameter tuning. IntroductionIt is generally known that there is a trade-off between exact methods and soft computing (heuristic) methods. Exact methods guarantee accurate/optimal solutions but require a relatively long computational time. Soft computing methods, on the other hand, do not guarantee optimal solution but require a relatively short computational time. For most medical imaging purposes, which often require constructing empirical models which preserve the shape and match data samples or approximate data samples and unknown entity at intermediate location, short computational time is preferred. Thus, we intend to explore the ability of Particle Swarm Optimization (PSO) methods in curve fitting for this purpose. PSO is an optimization technique proposed by Kennedy and Eberhart by means of particle swarm [9]. PSO incorporates swarming behaviours observed in flocks of birds, school of fish, swarm of bees and even social behaviour, from where the idea emerged. PSO is a population-based optimization tool, which could be implemented and applied easily to solve various function optimization problems, or problems that can be transformed to function optimization problems. As an algorithm, its fast convergence compares favourably with many global optimization algorithms like Genetic Algorithm [3], Simulated Annealing [6] and other global optimization algorithms. To apply PSO successfully, one of the key issues is finding ways to map problem solution into PSO article, which directly affects its feasibility and performance.

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Particle Swarm Optimization for Bézier Surface Reconstruction

Cited by 37 publications

References 19 publications

Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Particle swarm optimization for automatic creation of complex graphic characters

Conic Curve Fitting Using Particle Swarm Optimization: Parameter Tuning

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