无人水面艇目标图像自适应分割算法

Image adaptive segmentation algorithm for unmanned surface vehicle targets

针对水面目标与海天背景对比度变化大、景深差异明显的特点,提出一种改进的自适应Mean-Shift图像分割算法.首先通过估计参考点领域灰度值分布,自适应地得到空间域带宽,然后结合叶斯准则,自适应计算空间窗内灰度域带宽,实现目标与背景的自适应分割.分别抽取水面艇视频图像中,目标远、近距离以及清晰对比度不同的视频帧进行仿真测试,与传统分割算法对比研究,结果表明该算法可以有效实现水面目标图像分割.

Considering the large contrast changing of surface targets and sea-sky background and the obvious difference of field depth, an improved image segmentation algorithm based on self-adaptive Mean-Shift is proposed. Spatial bandwidths are adaptively computed according to the estimation of gray distribution around the reference point; then the gray-level bandwidths are adaptively computed with a novel Bayesian theory in the corresponding windows;and finally adaptive segmentation is obtained. In the experiment, both the close and distant target frames, as well as target frames of different contrast, are extracted respectively from the surface vehicle video sequence. Compared with the traditional segmentation algorithm, experimental results prove that the proposed algorithm can effectively complete segmentation of surface target images.