Computational intelligent color normalization for wheat plant images to support precision farming

Sulistyo, Susanto, Woo, Wai Lok and Dlay, Satnam (2016) Computational intelligent color normalization for wheat plant images to support precision farming. In: ICACI 2016 - 8th International Conference on Advanced Computational Intelligence, 14th - 16th February 2016, Chiang Mai, Thailand.

Full text not available from this repository.

Abstract

Image colors are considerably affected by the intensity of the light source. In this paper, we propose a color constancy method using neural networks fusion to normalize images captured under sunlight with a variation of light intensities. A genetic algorithm is also applied to optimize the color normalization. A 24-patch Macbeth color checker is utilized as the reference to normalize the images. The results of our proposed method is superior to other methods, i.e. the conventional gray world and scale-by-max methods, as well as linear model and single neural network method. Furthermore, the proposed method can be used to normalize wheat plant images captured under various light intensities.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	color constancy, genetic algorithm, gray world, neural networks, scale-by-max
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	05 Apr 2019 16:29
Last Modified:	10 Oct 2019 20:16
URI:	http://nrl.northumbria.ac.uk/id/eprint/38820

Downloads