Automatic plant identification using stem automata

Kan Li, Ying Ma, J. Príncipe
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引用次数: 3

Abstract

In this paper, we propose a novel approach to automatically identify plant species using dynamics of plant growth and development or spatiotemporal evolution model (STEM). The online kernel adaptive autoregressive-moving-average (KAARMA) algorithm, a discrete-time dynamical system in the kernel reproducing Hilbert space (RKHS), is used to learn plant-development syntactic patterns from feature-vector sequences automatically extracted from 2D plant images, generated by stochastic L-systems. Results show multiclass KAARMA STEM can automatically identify plant species based on growth patterns. Furthermore, finite state machines extracted from trained KAARMA STEM retains competitive performance and are robust to noise. Automatically constructing an L-system or formal grammar to replicate a spatiotemporal structure is an open problem. This is an important first step to not only identify plants but also to generate realistic plant models automatically from observations.
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使用茎自动机的自动植物识别
本文提出了一种利用植物生长发育动态或时空演化模型(STEM)自动识别植物物种的新方法。在线核自适应自回归移动平均(KAARMA)算法是核再现希尔伯特空间(RKHS)中的一个离散时间动力系统,用于从随机l系统生成的二维植物图像中自动提取的特征向量序列中学习植物发育语法模式。结果表明,多类KAARMA STEM能够基于生长模式自动识别植物物种。此外,从训练好的KAARMA STEM中提取的有限状态机保持了竞争性能,并且对噪声具有鲁棒性。自动构建l系统或形式语法来复制时空结构是一个开放的问题。这是重要的第一步,不仅可以识别植物,而且可以根据观测自动生成真实的植物模型。
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