Comparison of Optimisation Algorithms for Deformable Template Matching

Zografos, Vasileios

doi:10.1007/978-3-642-10520-3_105

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…In the literature, only two surveys can be found, which are just partially related to this one: [74] and [8]. The former provides a state-of-the-art survey on the application of the principles of genetic algorithms to medical image segmentation, only focusing onto this metaheuristic and considering all kinds of segmentation techniques.…”

Section: Image Segmentation Using Deformable Models and Metaheuristicsmentioning

confidence: 99%

“…In fact, most local optimization techniques perform effectively when the problem under consideration satisfies the said tight math-ematical constraints. However, when the search space is non-continuous, noisy, high-dimensional, non-convex or multimodal, those methods are consistently outperformed by stochastic optimization algorithms [7,8].…”

Section: Introductionmentioning

confidence: 99%

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A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

Mesejo

Ibáñez

Cordón

et al. 2016

Applied Soft Computing

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Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. Such a process requires that an optimization framework be defined whose most critical issues include: choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces; selecting the optimization and segmentation algorithms' parameters; choosing the representation for encoding prior knowledge on the image domain of interest; initializing the curve in a location which favors its convergence onto the boundary of the object of interest. All these problems are extensively discussed within this manuscript, with reference to the family of global stochastic optimization techniques that are generally termed metaheuristics, and are designed to solve complex optimization and machine learning problems. In particular, we present a complete study on the application of metaheuristics to image segmentation based on deformable models. This survey studies, analyzes and contextualizes the most notable and recent works on this topic, proposing an original categorization for these hybrid approaches. It aims to serve as a reference work which proposes some guidelines for choosing and designing the most appropriate combination of deformable models and metaheuristics when facing a given segmentation problem. After recalling the principles underlying deformable models and metaheuristics, we broadly review the different metaheuristic-based approaches to image segmentation based on deformable models, and conclude with a general discussion about methodological and design issues as well as future research and application trends.

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Section: Image Segmentation Using Deformable Models and Metaheuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

Mesejo

Ibáñez

Cordón

et al. 2016

Applied Soft Computing

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“…[15] If the cost function is obtained from experimental or sampled data, simplex search can be one of the best solutions. [16] In machine vision, simplex search has been applied to template matching, [17] face recognition, [18] and object 208 H. KIM ET AL.…”

Section: Introductionmentioning

confidence: 99%

Mixing and Simplex Search for Optimal Illumination in Machine Vision

Kim

Cho

Jin

et al. 2014

International Journal of Optomechatronics

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Mixed-color illumination affects the quality of images in industrial vision system and it is important to optimize color and intensity for image acquisition. This study used simplex search to find the optimal illumination in a short amount of time. A typical color mixer synthesized various color of lights by changing the inputs of RGB power LEDs and passing the lights through an optical system. The image quality under mixed-color illumination was calculated according to the sharpness. For the purpose of optimal illumination using simplex search, a probe network was organized with N þ 1probing points for N inputs. The shape of the probe network, simplex, was varied through procedures of extension, contraction, and shrinkage. The inputs of the color mixer were changed until the size of the simplex became smaller than a threshold. The simplex search was tested for commercial semiconductor patterns, and was useful for finding the optimal illumination.

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