Research On Computer Identification Of Standing Tree Branches' Occlusion

In order to solve the occlusion of tree branches, this research puts forward a computer identifying method of standing tree branches'occlusion based on a single image, and makes it capable for other computer vision research in forestry.Based on standing tree image processing, this paper proposes a genetic algorithms-based method of image segmentation, and gets satisfying result of segmentation. Using a mathematical morphology-based method, the paper achieves to extract the information of branches. And based on analyzing the growth characteristic of branches, and using straight line fitting technique, the paper proposes an identifying method of standing tree branches'occlusion based on a single image, and achieves to identify the occlusion of standing tree branches.The achievement and innovation of this paper is as follows:1. Based on analyzing each kind of general image segmentation technique, the paper proposes a genetic algorithms-based method of image segmentation to solve the limitation of the segmentation method in existence. The result of the experiment shows that not only can this method reflect the detail of the image, but also can well eliminate the yawp of background,and it is fairly effective in image segmentation of standing tree in complex environment.2. This paper proposes a mathematical morphology-based method of branches information extracting, using the basic operation of mathematical morphology—erosion and dilation, achieves to extract the information of trunk, and subtracting the trunk image from the binary image of tree, achieves to extract the information of branches.3. Based on analyzing the growth characteristic of branches, the paper proposes an identifying method of standing tree branches'occlusion based on a single image, and applies for national invention patent.The result of the experiment shows that the method proposed by this paper can preferably identify the occlusion of standing tree branches, and provides elements for computer vision application in forestry.