Zhang Wenhao, Hansen Mark F, Smith Melvyn, Smith Lyndon, Grieve Bruce
Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, T Block, Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, UK.
School of Electrical & Electronic Engineering, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
Comput Ind. 2018 Jun;98:56-67. doi: 10.1016/j.compind.2018.02.006.
Leaf venation extraction studies have been strongly discouraged by considerable challenges posed by venation architectures that are complex, diverse and subtle. Additionally, unpredictable local leaf curvatures, undesirable ambient illuminations, and abnormal conditions of leaves may coexist with other complications. While leaf venation extraction has high potential for assisting with plant phenotyping, speciation and modelling, its investigations to date have been confined to colour image acquisition and processing which are commonly confounded by the aforementioned biotic and abiotic variations. To bridge the gaps in this area, we have designed a 3D imaging system for leaf venation extraction, which can overcome dark or bright ambient illumination and can allow for 3D data reconstruction in high resolution. We further propose a novel leaf venation extraction algorithm that can obtain illumination-independent surface normal features by performing Photometric Stereo reconstruction as well as local shape measures by fusing the decoupled shape index and curvedness features. In addition, this algorithm can determine venation polarity - whether veins are raised above or recessed into a leaf. Tests on both sides of different leaf species with varied venation architectures show that the proposed method is accurate in extracting the primary, secondary and even tertiary veins. It also proves to be robust against leaf diseases which can cause dramatic changes in colour. The effectiveness of this algorithm in determining venation polarity is verified by it correctly recognising raised or recessed veins in nine different experiments.
叶脉提取研究一直受到严重阻碍，因为叶脉结构复杂、多样且细微，带来了诸多挑战。此外，不可预测的局部叶片曲率、不理想的环境光照以及叶片的异常状况可能与其他复杂因素并存。虽然叶脉提取在协助植物表型分析、物种形成和建模方面具有很大潜力，但迄今为止，其研究仅限于彩色图像采集和处理，而这些通常会受到上述生物和非生物变化的干扰。为了弥补这一领域的差距，我们设计了一种用于叶脉提取的3D成像系统，该系统可以克服黑暗或明亮的环境光照，并能够进行高分辨率的3D数据重建。我们还提出了一种新颖的叶脉提取算法，该算法可以通过执行光度立体重建获得与光照无关的表面法线特征，并通过融合解耦的形状指数和曲率特征来进行局部形状测量。此外，该算法可以确定叶脉极性——叶脉是凸起在叶片之上还是凹陷进叶片之中。对具有不同叶脉结构的不同叶片物种的两面进行测试表明，所提出的方法在提取主脉、次脉甚至三级叶脉方面都很准确。它还被证明对可能导致颜色发生巨大变化的叶部病害具有鲁棒性。该算法在确定叶脉极性方面的有效性通过在九个不同实验中正确识别凸起或凹陷的叶脉得到了验证。
