Genetic Algorithm to Set Active Contour

Rousselle, Jean-Jacques; Vincent, Nicole; Verbeke, Nicolas

doi:10.1007/978-3-540-45179-2_43

Cited by 7 publications

(7 citation statements)

References 2 publications

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“…This is consistent with the theoretical role of the regularising term. The results also show that the proposed algorithm estimates similar parameter values for similar objects of interest in similar images, which is much more stable than other parameter tuning algorithms based on stochastic search [12], which produce quite different parameter values for similar images or even the same images each time. Furthermore, since the proposed algorithm tunes the parameters by directly deriving and learning from the impact information based on the references without a stochastic search, it is less computationally expensive.…”

Section: B Experimental Resultsmentioning

confidence: 80%

“…Although this method avoids the problems of extremely low or high weights, the optimisation criterion is defined heuristically without consideration of any domain knowledge and the data characteristics. Genetic Algorithm has been utilised to tune the parameters of active contours by either a supervised or unsupervised approach [11], [12]. The genetic algorithm is used to vary the parameter values towards matching the evolving and reference contours in the training data.…”

Section: Introductionmentioning

confidence: 99%

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Learning to tune level set methods

Cai

Sowmya

2009

2009 24th International Conference Image and Vision Computing New Zealand

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Level set methods are very useful models in image segmentation, but require delicate adjustments of many parameters, which are typically determined empirically. This paper proposes a novel automatic method to address the challenge of parameter tuning for level set methods. It analyses the energy impact on the objects of interest during construction of the final contours, using a supervised machine learning approach. The method allows level set methods to automatically choose optimal values of the model parameters and extract objects at different granularities based on the training data. Experimental results demonstrate the capability of the method for accurate parameter tuning.

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Section: B Experimental Resultsmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Learning to tune level set methods

Cai

Sowmya

2009

2009 24th International Conference Image and Vision Computing New Zealand

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“…All these works propose a training mechanism to obtain the best value for those parameters using manually segmented examples as training data for the supervised learning process. Similarly, in [101], a supervised approach delivers a global set of parameter values as in the aforementioned proposals, while an unsupervised approach also determines a local set of parameter values.…”

Section: Active Contour Modelsmentioning

confidence: 99%

“…With respect to the selection mechanism, roulette wheel is the most frequently employed method [75,98,100,106,108,116,117,119,122], followed by tournament selection [92,97,101]. A rank-based selection operator was implemented in [77] while in [110,111] the authors differentiate between inter-and intra-population selection mechanisms.…”

Section: Operatorsmentioning

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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