基于人工神经网络的叶脉信息提取——植物活体机器识别研究Ⅰ

叶片的识别是识别植物的重要组成部分，特别在野外识别植物活体尤其重要。叶脉的脉序是植物的内在特征，包含有重要的遗传信息。但由于叶脉本身的多样性，利用单一特征的图像处理方法难以有效地提取叶脉。为了充分利用图像的信息，本文提出了一种基于人工神经网络的叶脉提取方法。该方法利用边缘梯度、局部对比度和邻域统计特征等10个参数来描述像素的邻域特征，并将其作为神经网络的输入层。实验结果表明，与传统方法相比，经过训练的神经网络能够更准确地提取叶脉图像，为进一步的叶片识别打下了良好的基础。

Extraction of Leaf Vein Features Based on Artificial Neural Network — Studies on the Living Plant Identification Ⅰ

Leaf recognition is an important step for plant computerized identification, especially for field living plants. Previous researches were mainly focused on leaf recognition by utilizing the peripheral contour of the leaf while ignoring the leaf venation that actually contains important genetic information.Conventional thresholding-based methods cannot extract the information accurately due to high diversity of leaf veins. In this paper, an approach based on artificial neural network learning is proposed to extract leaf venation. Ten features including edge gradients, local contrast and statistical features are extracted from a window centered at the image pixel and used to train a neural network classifier. Compared with conventional thresholding-based methods, the trained neural network is capable of extracting more accurate modality of leaf venation for subsequent leaf recognition.