Threshold segmentation algorithm for infrared small target in agriculture and forestry fire
A novel image segmentation for infrared small target of agriculture and forestry fire is proposed in this paper. Usually, Maximum Variance Image Segmentation method (Otsu) is a popular non parametric method in image segmentation. However, it needs a lot computation and has poor real time quality. Thus it is hard to be wide applied in many situations. To over come this issue, a constructive approach to obtain optimal threshold of between class variance as fitness function for Otsu by particle swarm optimization (PSO) reduce the amount of computation and improve real time performance. The performance of the proposed method is evaluated through infrared small target of agriculture and forestry fire. The experimental results demonstrate the effectiveness of the proposed method.
2016 35th Chinese Control Conference (CCC)