一种改进的基于MRF的树木图像提取方法

在自然场景中拍摄的树木图像包含了丰富的信息，并受环境、光照、天气、噪声等的干扰，树木本身及其周围景物的多样性使得自然场景中的树木图像提取成为一项复杂的、探索性很强的工作。本文采用自然图像抠图技术进行树木图像的提取，较好地解决了树木图像内部存在大量空洞和透明现象的问题；提出了关注区域的概念，并引入区域生长的方法，从简化三分图划分、尽可能多地确定前景像素点和减少未知区域待运算像素数目3个方面对基于马尔可夫随机场（MRF）的抠图方法进行了改进。实验结果表明:改进的基于MRF的树木图像抠图算法能够有效地提取树木图像，并简化了人机交互过程，增强了颜色的准确性，同时使运算速度大幅度提高。 

A modified algorithm used in tree image extraction based on MRF model

Tree image extraction,which is more than basic,provides the fundamental data and technical support for these studies. However,tree image extraction is also a difficult problem since the tree photo shooting in the natural scene contains a wealth of information and is subject to the environment,light, weather,noise and other interference. The diversity of trees and its surrounding scenery make tree image extraction in the natural scene complex and challenging. This paper adopts the natural image matting to extract the tree image,better solve the problems of tree image internal void and image transparent, propose the concept of regions of interest,and introduce the region-growing method to improve the matting based on Markov Random Field ( MRF ) from the aspects of simplifying the division of trimap, determining the foreground pixels as much as possible and reducing the number of pixels of the unknown region. The experiment shows that this method can effectively extract the tree image,simplify the human machine interaction process,and enhance color accuracy,and at the same time increase computing speed greatly.