Studies On Tree Image Extraction In A Complex Background

Tree image extraction is a technology to separate an object tree from the surrounding landscape in a photograph which is shot on the ground, and to obtain its characteristics data. With the continuous development of computer information technology, the thinking of Precision Forestry is proposed. Forestry researchers made related topics such as forestry stereo vision measurement, accurate to the target application of pesticides tree, image-based visual reconstruction of trees, growth, condition assessment, automatic identification and classification of tree species. And they will continue to study and explore these fields. Tree image extraction provide the basis data and technical support for the above research and applications, it is very important and still a difficulty problem. The background of a photograph which is shot in the natural scene is uncertain and complex. The diversity of trees and surrounding scenery make the work to extract an object tree in the complex background is hard and highly groping. This research has important applied value and practical significance. This paper take the machine identified as a research background, for the purposes of improve the speed, quality of matting and further reduce the human interaction involvement. Based on existing technology, in the image segmentation and natural image matting for complex background image of the unique characteristics of trees, make research and implementation of tree appearance feature extraction method for complex background image of the unique characteristics of trees.Firstly, the paper researches the image segmentation method to extract the image of trees in the applied. Image segmentation method can be divided into threshold segmentation, edge detection, region segmentation. With the continuous development of imaging technology, some segmentation methods are proposed such as image segmentation based on specific theory, combination of multi-segmentation method and human-computer interaction image segmentation. This paper analysis the shortcomings, limited and improvements according to the segmentation results of the current main method. Then, according to the main features of the trees, this paper carried out image segmentation based on color features and texture features. The characteristics of color is greatest different between the trees and the environment. The use of color features is conducive to separation of non-green plants and non-green background. Canopy of different tree species often have significant texture feature differences, also trees and other green plants in the background have significant texture difference. Therefore, statistical analysis of texture can be used to effectively split the images of trees. In this paper, first transform the color space of images from RGB to Lab, then separate a channel, extract image texture feature according to the Gray Level Co-occurrence Matrix algorithm, thought segment gray image of color and texture features and apply mathematical morphological amendment. This method is relatively simple, fast and has markedly accuracy compared to traditional methods. However, the method could do nothing if the background is too complicated.On top of the research to extract tree images by means of image segmentation, there has studied the application, of natural image matting technology in tree image extraction. In an image of trees, the edge of a leaf in nature scene is usually even smaller than a pixel, or there are green plants around very similar to the goal objectives, etc. In these complex cases, image segmentation method cann't work efficient. There summed up the limitations of several existing extracting methods through analysis and comparison, and realized GrabCut algorithm.Based on the above analysis, this paper presents an improved natural image matting techniques based Markov Random Field. There would be a lot empty in foreground of tree image, also accompanied by the phenomenon of transparent and translucent features. Taking into account this situation, this paper improved MRF method by simplified one-third figure, broken down as much as possible to determine the prospects for the unknown pixel and reduce the number of three point of view of regional pixel-based. This method reduces the complexity of human-computer interaction, markedly enhanced the color accuracy. Also enhance the computing speed more than 34%, it is a very efficient and practical trees image matting method.Finally, the paper compares and makes research of the image segmentation method and the natural image matting method for the tree image extraction technology according to the results. By statistical analysis, the differences of two methods were expressed as:image segmentation method is relatively simple and relatively fast operation, without human interaction, but the relatively low accuracy rate; natural image matting methods are complexity, computational speed slower, need the right amount of human interaction, but the extraction rate is high accuracy.