Abstract
An Edge of an image is a sudden change in the intensity of an image. Edge detection is process of finding the edges of an image. Edge detection is one of the image preprocessing techniques which significantly reduces the amount of data and eliminates the useless information by processing the important structural properties in an image. There are many traditional algorithms used to detect the edges of an image. Some of the important algorithms are Sobel, Prewitt, Canny, Roberts etc. A Hybrid approach for Image edge detection using Neural Networks and Particle swarm optimization is a novel algorithm to find the edges of image. The training of neural networks follows back propagation approach with particle swarm optimization as a weight updating function. 16 visual patterns of four bit length are used to train the neural network. The optimized weights generated from neural network training are used in the testing process in order to get the edges of an image.
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Naidu, D.L., Rao, C.S., Satapathy, S. (2015). A Hybrid Approach for Image Edge Detection Using Neural Network and Particle Swarm Optimization. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_1
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DOI: https://doi.org/10.1007/978-3-319-13728-5_1
Publisher Name: Springer, Cham
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