计算机工程

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基于生长模型的苗期大豆植株三维重建

宋祺鹏 1,唐晶磊 1,2,辛菁 2   

  1. (1.西北农林科技大学 信息工程学院,陕西 杨凌 712100; 2.陕西省复杂系统控制与智能信息处理重点实验室,西安 710048)
  • 收稿日期:2016-07-04 出版日期:2017-05-15 发布日期:2017-05-15
  • 作者简介:宋祺鹏(1990—),男,硕士研究生,主研方向为虚拟现实技术;唐晶磊(通信作者)、辛菁,副教授、博士、硕士生导师。
  • 基金项目:
    国家“863”计划项目(2013AA10230402);国家自然科学基金(31101075);陕西省农业科技创新与攻关项目(2015NY049);西安市科技计划项目(NC1504(2))。

3-dimensional Reconstruction for Soybean Plant of Seedling Stage Based on Growth Model

SONG Qipeng  1,TANG Jinglei  1,2,XIN Jing  2   

  1. (1.College of Information Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China; 2.Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xi’an 710048,China)
  • Received:2016-07-04 Online:2017-05-15 Published:2017-05-15

摘要: 为实现大豆植株的三维模型重建,以苗期的中黄13号大豆植株为研究对象,利用三次参数样条曲线方程拟合叶片轮廓,基于Delaunay三角剖分的逐点插入思想实现不规则多边形的网格化,在叶脉纹理能量测量的研究方面,考虑到现有方法对纹理的方向和尺度信息表现不构充分,提出基于Gabor滤波器的纹理能量特征提取方法,并采用空间金字塔匹配算法进行纹理的分析与渲染,结合参数化的L系统与有限状态自动机模型,实现了苗期大豆植株的形态可视化。实验结果表明,与传统的大豆植株三维重建方法相比,该方法能有效地描述纹理渐变线索与场景深度之间的关系,更好地保持大豆植株的拓扑结构和轮廓特征。

关键词: 三次样条曲线, Gabor滤波器, 纹理能量, 局部特征, 边缘检测

Abstract: In order to realize 3-dimensional model reconstruction of soybean, this paper treats the Yellow No.13 soybean in seedling stage as research object. It takes the advantage of cubic parametric spline curve to fit the outline of the leaf, according to the incremental insertion algorithm of Delaunay to realize the triangular mesh of irregular polygon. In research of texture energy, considering the existing methods for the information representation of texture orientation and scale is insufficient, making use of Gabor filter to extract the texture energy feature and Spatial Pyramid Matching(SPM) algorithm to analyze and render texture, combining L-system and the finite state automaton model to realize the visualization of the soybean. Compared with traditional 3D reconstruction method, experimental results show that this method can effectively describe the relationship between the texture clues and the depth of scene, better to keep the topology structure and contour features of the soybean.

Key words: cubic spline curve, Gabor filter, texture energy, local feature, border detection

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