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|Table of Contents|

基于Graph Cut算法的多株立木轮廓提取方法(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2018年06期
Page:
91-98
Column:
研究论文
publishdate:
2018-12-15

Article Info:/Info

Title:
Multiple trees contour extraction method based on Graph Cut algorithm
Author(s):
YANG Tingting1GUAN Fangli2XU Aijun1*
(1.School of Information Engineering, Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Zhejiang A & F University, Hangzhou 311300, China; 2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)
Keywords:
Graph Cut algorithm contour extraction improved Canny operator image segmentation
Classification number :
S781; TP391.41
DOI:
10.3969/j.issn.1000-2006.201804012
Document Code:
A
Abstract:
【Objective】 Because of the complexity of the natural environment, current tree contour extraction results are not satisfactory. This paper presents a method to extract a contour of multiple trees based on a Graph Cut algorithm to realize the boundary segmentation of multi-target trees in a single photo. 【Method】 First, this method enhances image details of each channel under RGB color space captured in the experiment by color histogram equalization. The graph of the s-t network is constructed using a Graph Cut algorithm to look for min-cut, and the image segmentation problem is transformed into the minimization of the energy function by marking the foreground and background pixels to achieve a single photo of many trees with preliminary image segmentation. Then, adaptive thresholding of gray-scale transformation is applied to the multiple-segmentation images to realize binarization of the images and morphological corrosion expansion, and the opening and closing operation processing of the binary images is used to achieve the filling, denoising, and smoothing of trees. On the basis of this morphological process, combined with the improved Canny operator edge detection technology, bilateral filtering is used instead of Gaussian filtering to enhance the boundary information to obtain a preliminary tree contour. Finally, according to the geometric position invariance of the photo’s trees, we use the geometric reconstruction method to express the features of target trees and judge their topological relationships. If there are topological relationship errors, we iterate the Graph Cut algorithm and geometry reassembly method again to obtain a better target tree extraction result. 【Result】 In order to validate the effectiveness of this method experimentally, we collected tree images in a natural environment. The results showed that this method can effectively separate the contour of every tree under different lighting conditions. The average error rate(Af)was 5.62%, the false positive rate(RFP)was 4.49%, and the false negative rate(RFN)was 4.33%, which was better than those obtained by the traditional OTSU segmentation algorithm(41.40%, 26.73% and 10.99%, respectively), the K-means clustering algorithm(49.97%, 35.02% and 11.92%), and the C-V Plane Models(28.43%, 20.53% and 13.38% ). 【Conclusion】 In a complex natural environment, a Graph Cut algorithm based on human interaction can effectively separate vertical boundaries. The results provide a reference for the visualization and reconstruction of trees and feature extraction.

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Last Update: 2018-11-30