Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm

Authors:
Alper Baştürk;Enis Günay
Affiliations:
Department of Computer Engineering, Erciyes University, Kayseri 38039, Turkey;Department of Electrical and Electronics Engineering, Erciyes University, Kayseri 38039, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 8
Cited 11

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An Operator Which Locates Edges in Digitized Pictures

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Optimized distance metrics for differential evolution based nearest prototype classifier

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A differential evolution algorithm for joint replenishment problem using direct grouping and its application

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A comparison of nature inspired algorithms for multi-threshold image segmentation

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Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data sets

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A novel particle swarm optimisation approach to detecting continuous, thin and smooth edges in noisy images

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Hardware implementation of new bell-shaped pulse mode neural network with on-chip learning and application to image processing

International Journal of High Performance Systems Architecture

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Abstract

A cellular neural network (CNN) based edge detector optimized by differential evolution (DE) algorithm is presented. Cloning template of the proposed CNN is adaptively tuned by using simple training images. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature. Simulation results indicate that the proposed CNN operator outperforms competing edge detectors and offers superior performance in edge detection in digital images.