Flame Image Segmentation Algorithm Based on Motion and Color Saliency

Abstract

This paper proposed a flame segmentation algorithm based on the saliency of motion and color. First, feature point detection is performed on the video image using the scale-invariant feature transform (SIFT) algorithm, and the optical flow field of moving object in the adjacent frame is acquired by the optical flow method. According to the optical flow vector difference between the target pixel point and the surrounding neighborhood pixels, the motion saliency map is obtained based on the Munsell color system. Then, the LSI flame color statistical model based on the Lab and HSI space is used to extract color saliency map of video images. Finally, under the Bayes framework, the motion saliency map and the color saliency map are fused in an interactive manner to obtain the final flame segmentation map. Experimental results show that the proposed algorithm can effectively segment the flame image in different scenarios.