Using Structured Lighting and Shadow for Leaf Segmentation

2007 ◽  
Author(s):  
Peter Neil Kennedy ◽  
Scott D Noble
Keyword(s):  
Structured Lighting ◽  
Leaf Segmentation

Automatic stem-leaf segmentation of maize shoots using three-dimensional point cloud

2021 ◽  
Vol 187 ◽  
pp. 106310
Author(s):  
Teng Miao ◽  
Chao Zhu ◽  
Tongyu Xu ◽  
Tao Yang ◽  
Na Li ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Leaf Segmentation ◽  
Stem Leaf

scholarly journals PlantCV v2: Image analysis software for high-throughput plant phenotyping

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4088 ◽  
Author(s):  
Malia A. Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C. Berry ◽  
Steven T. Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals Software package for testing three-dimensional shape measurement methods using structured lighting

2021 ◽  
Vol 2057 (1) ◽  
pp. 012087
Author(s):  
S V Dvoynishnikov ◽  
V O Zuev ◽  
I K Kabardin ◽  
D V Kulikov ◽  
V V Rahmanov
Keyword(s):  
Software Package ◽  
Three Dimensional ◽  
Measurement Methods ◽  
Shape Measurement ◽  
Structured Lighting ◽  
Lighting Conditions ◽  
Ambient Lighting ◽  
Dimensional Shape ◽  
Shadow Maps ◽  
Three Dimensional Shape

Abstract This work aims at creating a universal software package for the development and testing of triangulation methods using structured lighting for measuring the three-dimensional geometry of objects in difficult ambient lighting conditions. As a result, a software package meeting the stated requirements is created. Lighting is based on the Fong model. A method for preloading objects is implemented to optimize the operation of the software package. An accelerated method for creating shadow maps is proposed and implemented. The developed software package is shown to successfully perform all required functions.

Full-surface 3-D reconstruction based on surround structured lighting

2019 ◽  
Author(s):  
Hao Xu ◽  
Wei Yin ◽  
Chen Yang ◽  
Jiachao Li ◽  
Shijie Feng ◽  
...  
Keyword(s):  
Structured Lighting

scholarly journals Underwater 2D Image Acquisition Using Sequential Striping Illumination

2019 ◽  
Vol 9 (11) ◽  
pp. 2179
Author(s):  
Benxing Gong ◽  
Guoyu Wang
Keyword(s):  
Image Acquisition ◽  
Scattered Light ◽  
Forward Scattering ◽  
Image Recovery ◽  
Underwater Imaging ◽  
Scattering Problems ◽  
Structured Lighting ◽  
Underwater Image ◽  
Scattering Effects ◽  
Scanning Systems

Structured lighting techniques have increasingly been employed in underwater imaging, where scattering effects cannot be ignored. This paper presents an approach to underwater image recovery using structured light as a scanning mode. The method tackles both the forward scattering and back scattering problems. By integrating each of the sequentially striping illuminated frame images, we generate a synthesized image that can be modeled on the convolution of the surface albedo and the illumination function. Thus, image acquisition is issued as a problem of image recovery by deconvolution. The convolutional model has the advantage of integrating the forward scattering light into a recovered image so as to eliminate image blur. Notably, the removal of the back scattered light from each frame image can be easily realized by a virtual aperture to limit the field of view; the same principle as of the synchronous scanning systems in underwater imaging. Herein, the implementation of the proposed approach is described, and the results of the underwater experiments are presented.

scholarly journals Leaf segmentation in plant phenotyping: a collation study

2015 ◽  
Vol 27 (4) ◽  
pp. 585-606 ◽  
Author(s):  
Hanno Scharr ◽  
Massimo Minervini ◽  
Andrew P. French ◽  
Christian Klukas ◽  
David M. Kramer ◽  
...  
Keyword(s):  
Plant Phenotyping ◽  
Leaf Segmentation
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