首页> 外文会议>International Conference on Digital Image Computing Techniques and Applications >A Riemannian Elastic Metric for Shape-Based Plant Leaf Classification
【24h】

A Riemannian Elastic Metric for Shape-Based Plant Leaf Classification

机译:基于形状的植物叶分类的黎曼弹性指标

获取原文

摘要

The shapes of plant leaves are of great importance to plant biologists and botanists, as they can help to distinguish plant species and measure their health. In this paper, we study the performance of the Squared Root Velocity Function (SRVF) representation of closed planar curves in the analysis of plant-leaf shapes. We show that it provides a joint framework for computing geodesics (registration) and similarities between plant leaves, which we use for their automatic classification. We evaluate its performance using standard databases and show that it outperforms significantly the state-of-the-art descriptor-based techniques. Additionally, we show that it enables the computation of shape statistics, such as the average shape of a leaf population and its principal directions of variation, suggesting that the representation is suitable for building generative models of plant- leaf shapes.
机译:植物叶子的形状对于植物生物学家和植物学家具有重要意义,因为它们可以帮助区分植物物种并衡量其健康。 本文研究了植物叶形状分析中闭式平面曲线的平方根速度函数(SRVF)表示的性能。 我们表明它为计算了植物叶片之间的大动物(注册)和相似性提供了一个联合框架,我们用于自动分类。 我们使用标准数据库评估其性能,并表明它优于基于最先进的描述符的技术。 此外,我们表明它能够计算形状统计,例如叶片种群的平均形状及其主要变化方向,表明该表示适用于建立植物叶片形状的生成模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号