植物叶片图像的识别是计算机视觉和图像处理技术在生物学和现代农业中的一个重要应用.其挑战性在于植物叶片种类数量巨大，且许多叶片图像具有很大的类间相似性，使得描述叶片图像的类间差异变得非常困难.提出一种称为高斯卷积角的叶片形状描述方法.该方法用高斯函数与叶片轮廓点的左右邻域向量的卷积产生高斯卷积角，再通过改变高斯函数的尺度参数，生成多尺度的高斯卷积角，组成特征向量.组合各轮廓点的特征向量，构成一个特征向量集合，作为叶片形状的描述子.两幅叶片图像的相似性可以简单地通过计算其高斯卷积角特征向量集合间的Hausdorff距离来进行度量.高斯卷积角描述子具有平移、旋转、缩放和镜像变换的内在不变性，该不变性从理论上得到了证明.该描述子还具有由粗到细的描述叶片形状的优良特性，使得其具有很强的叶片辨识能力.通过用中外两个公开的叶片图像数据集进行算法性能测试，实验结果表明，该方法优于现有的其他同类方法，从而验证了该方法的有效性.

Plant leaf image recognition is one of important applications of computer vision and image processing technology to biology and modern agriculture. It is a challenging problem due to the large size of the plant species community and great inter-class similarity, which makes it very difficult to describe the variants between classes of leaf images. In this study, a novel shape description method, Gaussian convolution angle, is proposed for identifying leaf image. For each contour point, its left and right neighborhood vectors are convolved with a Gaussian function respectively to form Gaussian convolution angle. By changing the scale parameter of the Gaussian function, multiscale Gaussian convolution angles are derived to form a feature vector. Combing the feature vectors of all the contour points, a set of feature vectors is built for describing leaf shape. The similarity of the two leaf images can be simply measured by calculating the Hausdorff distance between their feature vector sets. The proposed Gaussian convolution angle descriptor is inherently invariant to translation, rotation, scaling, and mirror transformation which have been theoretically proved in this study. The descriptor also has the excellent characteristics of describing the leaf shape from coarse to fine, which makes it have a strong ability to identify the leaf. Two publicly available leaf image datasets are used to test the performance of the proposed method. The experimental results show that the proposed method outperforms the state-of-the-art methods on leaf image recognition, which indicates the effectiveness of the proposed method.

TP391

国家自然科学基金（61372158）；国家重点研发计划（2017YFD0700501）；江苏省自然科学基金（BK20181414）；江苏省高校优秀科技创新团队项目（2017-15）；江苏省高校自然科学研究重大项目（18KJA52004）；智能机器人湖北省重点实验室开放基金（HBIR202001）