Multilevel image thresholding selection using the artificial bee colony algorithm
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
SAR image segmentation based on Artificial Bee Colony algorithm
Applied Soft Computing
Medical image thresholding using WQPSO and maximum entropy
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Hi-index | 0.00 |
The contrast of the underwater images is often extraordinarily low due to the ray, assimilating of water, illuminating condition and so on. It is not good for the pretreatment like edge detection and image segmentation. The theory of entropy has been widely used in the pre-process of under water images. However the time-consuming computation is often an obstacle in real time application systems. In this paper, the image thresholding approach with the index of entropy maximization of the grayscale histogram based on a new optimization algorithm, namely, the particle swarm optimization (PSO) algorithm is proposed to deal with underwater image. The experiments of segmenting the underwater images are illustrated to show that the proposed method can get ideal segmentation result with less computation cost.