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.

复杂，多样和微妙的脉络结构所带来的巨大挑战已大大阻止了叶脉提取研究。此外，不可预测的局部叶片曲率，不良的环境光照以及异常的叶片状况可能与其他并发症并存。虽然叶脉提取物在协助植物表型，物种形成和建模方面具有很高的潜力，但迄今为止，其研究仅限于彩色图像的采集和处理，而彩色图像的采集和处理通常被上述生物和非生物变异所混淆。为了弥合该区域的空白，我们设计了一种用于叶片静脉抽取的3D成像系统，该系统可以克服黑暗或明亮的环境照明，并可以高分辨率地重建3D数据。我们进一步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学立体重建以及融合解耦的形状指数和弯曲度特征来获得局部形状度量，从而获得与照明无关的表面法线特征。此外，该算法可以确定静脉极性-静脉是在叶片上方凸起还是在叶片中凹陷。在不同叶片种类的两侧使用不同的通气结构进行的测试表明，该方法在提取初级，次级甚至第三级静脉方面是准确的。它也被证明对抵抗可能引起颜色急剧变化的叶片疾病具有抵抗力。通过在9个不同的实验中正确识别出凸起或凹陷的静脉，验证了该算法确定静脉极性的有效性。