Genetic algorithm approach to image segmentation using morphological operations

Yu, M.; Eua-Anant, Nawapak; Saudagar, Abdul Khader Jilani; Удпа, Лалита

doi:10.1109/icip.1998.999063

Cited by 19 publications

(11 citation statements)

References 3 publications

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“…This population-based search by the principle of natural selection and genetic recombination makes GA a powerful tool for optimization. Its applications in the image analysis area have already taken place (see [11]- [16]). …”

Section: Related Workmentioning

confidence: 99%

“…That means we can estimate their distributions separately. To do so, first, we compute a fitness-weight (16) for each solution in generation .…”

Section: ) Representation and Fitness Evaluationmentioning

confidence: 99%

“…Bhanu et al [14] improve the best chromosome in every generation using a hill climbing strategy. Yu et al [16] apply morphological operation to selected chromosomes before they are subject to crossover and mutation. Bhandarkar and Zhang [13] resort to the engineered conditioning (EC) operator.…”

Section: Hybridization With Local Searchmentioning

confidence: 99%

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Evolutionary Optimization With Markov Random Field Prior

Wang

2004

IEEE Trans. Evol. Computat.

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Abstract-This paper discusses an evolutionary algorithm in which the constituent variables of a solution are modeled by a Markov random field (MRF). We maintain a population of potential solutions at every generation and for each solution a fitness value is calculated. The evolution, however, is not achieved through genetic recombination. Instead, each variable in a solution will be updated by sampling from its probability distribution. According to the MRF prior, local exploitation is encoded in the conditional probabilities. For evolutionary exploration, we estimate the probabilities as fitness-weighted statistics. These two kinds of search are combined smoothly in our algorithm. We compare it with two representative algorithms [iterated conditional modes (ICM) and simulated annealing (SA)] on noisy and textured image segmentation. Remarkable performance is observed.Index Terms-Combinatorial optimization, estimation of distribution algorithms, evolutionary computation, genetic algorithms (GAs), hybrid search algorithm, image segmentation, Markov random fields (MRF).

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Section: Related Workmentioning

confidence: 99%

“…That means we can estimate their distributions separately. To do so, first, we compute a fitness-weight (16) for each solution in generation .…”

Section: ) Representation and Fitness Evaluationmentioning

confidence: 99%

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Evolutionary Optimization With Markov Random Field Prior

Wang

2004

IEEE Trans. Evol. Computat.

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“…Yu et al [45] proposed a simple approach to image segmentation using GAs in conjunction with morphological operations. The images considered were composed of objects with a constant intensity embedded in a homogeneous background.…”

Section: Gas For MM Filters and Algorithm Designmentioning

confidence: 99%

Morphological algorithm design for binary images using genetic programming

Quintana

Poli

Claridge

2006

Genet Program Evolvable Mach

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Abstract. This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.

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“…Many stochastic optimization schemes that aim at a global maximum have been reported, among which are simulated annealing methods [9], [24], [32] and evolutionary algorithms [12], [18]- [20], [27], [36]. As part of some evolutionary approaches also certain domain specific recombination operators have been reported [7], [8], [25], [35].…”

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confidence: 99%

A cellular coevolutionary algorithm for image segmentation

Veenman

Reinders

Backer

2003

IEEE Trans. on Image Process.

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Abstract-Clustering is inherently a difficult problem, both with respect to the definition of adequate models as well as to the optimization of the models. In this paper we present a model for the cluster problem that does not need knowledge about the number of clusters a priori. This property is among others useful in the image segmentation domain, which we especially address. Further, we propose a cellular coevolutionary algorithm for the optimization of the model. Within this scheme multiple agents are placed in a regular two-dimensional (2-D) grid representing the image, which imposes neighboring relations on them. The agents cooperatively consider pixel migration from one agent to the other in order to improve the homogeneity of the ensemble of the image regions they represent. If the union of the regions of neighboring agents is homogeneous then the agents form alliances. On the other hand, if an agent discovers a deviant subject, it isolates the subject. In the experiments we show the effectiveness of the proposed method and compare it to other segmentation algorithms. The efficiency can easily be improved by exploiting the intrinsic parallelism of the proposed method.

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Genetic algorithm approach to image segmentation using morphological operations

Cited by 19 publications

References 3 publications

Evolutionary Optimization With Markov Random Field Prior

Evolutionary Optimization With Markov Random Field Prior

Morphological algorithm design for binary images using genetic programming

A cellular coevolutionary algorithm for image segmentation

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