Cellular edge detection: Combining cellular automata and cellular learning automata

Mofrad, Mohammad Hasanzadeh; Sadeghi, Sana; Rezvanian, Alireza; Meybodi, Mohammad Reza

doi:10.1016/j.aeue.2015.05.010

Cited by 43 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LA have been successfully applied to a number of applications including image processing, 42,43 pattern recognition, 44 wireless sensor networks, 45 parameter adaption, 12,16 function optimization, 46 multi objective optimization, 47 dynamic optimization, 48 Sampling from Complex Networks, 49 graph problems, 50,51 and information retrieval. 52…”

Section: Learning Automatamentioning

confidence: 99%

See 1 more Smart Citation

LADE: Learning Automata Based Differential Evolution

Mahdaviani

Kordestani

Rezvanian

et al. 2015

Int. J. Artif. Intell. Tools

Self Cite

View full text Add to dashboard Cite

Many engineering optimization problems do not standard mathematical techniques, and cannot be solved using exact algorithms. Evolutionary algorithms have been successfully used for solving such optimization problems. Differential evolution is a simple and efficient population-based evolutionary algorithm for global optimization, which has been applied in many real world engineering applications. However, the performance of this algorithm is sensitive to appropriate choice of its parameters as well as its mutation strategy. In this paper, we propose two different underlying classes of learning automata based differential evolution for adaptive selection of crossover probability and mutation strategy in differential evolution. In the first class, genomes of the population use the same mutation strategy and crossover probability. In the second class, each genome of the population adjusts its own mutation strategy and crossover probability parameter separately. The performance of the proposed methods is analyzed on ten benchmark functions from CEC 2005 and one real-life optimization problem. The obtained results show the efficiency of the proposed algorithms for solving real-parameter function optimization problems.

show abstract

Section: Learning Automatamentioning

confidence: 99%

“…6 that ILADERεP has a smaller increasing rate than the EPSDE. Moreover, ILADERεP is able to save computation time by 43…”

Section: Computational Complexity Of Iladerεpmentioning

confidence: 99%

LADE: Learning Automata Based Differential Evolution

Mahdaviani

Kordestani

Rezvanian

et al. 2015

Int. J. Artif. Intell. Tools

Self Cite

View full text Add to dashboard Cite

show abstract

“…Uguz et al [49] proposed a thresholding technique of edge detection based on fuzzy cellular automata transition rules enhanced using Particle Swarm Optimization. Hasanzadeh et al [30] introduced a novel CA local rule with an adaptive neighborhood in order to produce the edge map of image. In contrast to common fixed neighborhood CAs, the proposed adaptive algorithm employs both von Neumann and Moore neighborhoods in an adaptive formulation.…”

Section: Introductionmentioning

confidence: 99%

Salt-n-pepper noise filtering using Cellular Automata

Tourtounis,

Mitianoudis,

Sirakoulis

2017

Preprint

View full text Add to dashboard Cite

Cellular Automata (CA) have been considered one of the most pronounced parallel computational tools in the recent era of nature and bio-inspired computing. Taking advantage of their local connectivity, the simplicity of their design and their inherent parallelism, CA can be effectively applied to many image processing tasks. In this paper, a CA approach for efficient salt-n-pepper noise filtering in grayscale images is presented. Using a 2D Moore neighborhood, the classified "noisy" cells are corrected by averaging the non-noisy neighboring cells. While keeping the computational burden really low, the proposed approach succeeds in removing high-noise levels from various images and yields promising qualitative and quantitative results, compared to state-of-the-art techniques.

show abstract