Photometric stereo for three-dimensional leaf venation extraction

Zhang, Wenhao; Hansen, Mark F; Smith, Melvyn; Smith, Lyndon; Grieve, Bruce

Abstract

© 2018 Elsevier B.V. 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.

• Journal Article Type: Article
• Acceptance Date: Feb 22, 2018
• Online Publication Date: Mar 8, 2018
• Publication Date: Jun 1, 2018
• Deposit Date: Mar 12, 2018
• Publicly Available Date: Jan 10, 2020
• Journal: Computers in Industry
• Print ISSN: 0166-3615
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 98
• Pages: 56-67
• DOI: https://doi.org/10.1016/j.compind.2018.02.006
• Keywords: leaf venation, leaf disease, 3D imaging, shape index, photometric stereo, ridge detection
• Public URL: https://uwe-repository.worktribe.com/output/876683
• Publisher URL: https://doi.org/10.1016/j.compind.2018.02.006
• Contract Date: Mar 12, 2018