scholarly journals Plant Leaf Position Estimation with Computer Vision

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5933
Author(s):  
James Beadle ◽  
C. James Taylor ◽  
Kirsti Ashworth ◽  
David Cheneler
Keyword(s):  
Computer Vision ◽  
Three Dimensional ◽  
Digital Camera ◽  
Position Estimation ◽  
Infrared Light ◽  
Depth Sensors ◽  
Plant Canopies ◽  
Sensor Platforms ◽  
Trained Neural Network ◽  
Infrared Methods

Autonomous analysis of plants, such as for phenotyping and health monitoring etc., often requires the reliable identification and localization of single leaves, a task complicated by their complex and variable shape. Robotic sensor platforms commonly use depth sensors that rely on either infrared light or ultrasound, in addition to imaging. However, infrared methods have the disadvantage of being affected by the presence of ambient light, and ultrasound methods generally have too wide a field of view, making them ineffective for measuring complex and intricate structures. Alternatives may include stereoscopic or structured light scanners, but these can be costly and overly complex to implement. This article presents a fully computer-vision based solution capable of estimating the three-dimensional location of all leaves of a subject plant with the use of a single digital camera autonomously positioned by a three-axis linear robot. A custom trained neural network was used to classify leaves captured in multiple images taken of a subject plant. Parallax calculations were applied to predict leaf depth, and from this, the three-dimensional position. This article demonstrates proof of concept of the method, and initial tests with positioned leaves suggest an expected error of 20 mm. Future modifications are identified to further improve accuracy and utility across different plant canopies.

Reverse Design and Application of Irregular Planar Part Based on Image Processing

2012 ◽  
Vol 229-231 ◽  
pp. 1706-1709
Author(s):  
Jian Jun Yin ◽  
Jia Qing Lin ◽  
S.Mittal Gauri ◽  
Shuang Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Aided Design ◽  
Detection System ◽  
Design Method ◽  
Three Dimensional ◽  
Digital Camera ◽  
Solid Modeling ◽  
Vector Model ◽  
Planar Part

By using a computer vision detection system to obtain high resolution images of a machine part, a kind of reverse design method of solid modeling of irregular planar part with aided implementation of computer vision was proposed in this paper, which integrates image processing function of Matlab software with solid modeling function of computer aided design (CAD) software. The method used a calibrated digital camera to get the image of the tested part, a three-dimensional entity vector model may be built up after image inversion, edge detection, vectorization process of binary image and size matching were operated sequentially. The results of image reverse design showed that it is an easy and convenient way to reverse irregular planar parts based on image processing. One of its remarkable advantages is the saving of design period and the reduction of design cost. Its measurement error can be controlled within 0.1 mm, and can meet general precision requirement of application occasions. Reversed parts may provide a model basis for further analysis on mechanism assembling and motion simulation.

scholarly journals An Improved 3D Shape Recognition Method Based on Panoramic View

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Zheng ◽  
Jian Sun ◽  
Le Zhang ◽  
Wei Chen ◽  
Huanhuan Fan
Keyword(s):  
Computer Vision ◽  
Shape Recognition ◽  
Three Dimensional ◽  
Panoramic View ◽  
Shape Representations ◽  
Practical Applications ◽  
3D Shape ◽  
Depth Sensors ◽  
Novel Method ◽  
3D Shapes

Recognition of three-dimensional (3D) shape is a remarkable subject in computer vision systems, because of the lack of excellent shape representations. With the development of 2.5D depth sensors, shape recognition is becoming more important in practical applications. Many methods have been proposed to preprocess 3D shapes, in order to get available input data. A common approach employs convolutional neural networks (CNNs), which have become a powerful tool to solve many problems in the field of computer vision. DeepPano, a variant of CNN, converts each 3D shape into a panoramic view and shows excellent performance. It is worth paying attention to the fact that both serious information loss and redundancy exist in the processing of DeepPano, which limits further improvement of its performance. In this work, we propose a more effective method to preprocess 3D shapes also based on a panoramic view, similar to DeepPano. We introduce a novel method to expand the training set and optimize the architecture of the network. The experimental results show that our approach outperforms DeepPano and can deal with more complex 3D shape recognition problems with a higher diversity of target orientation.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

scholarly journals Proposing 3D Thermal Technology for Heritage Building Energy Monitoring

2021 ◽  
Vol 13 (8) ◽  
pp. 1537
Author(s):  
Antonio Adán ◽  
Víctor Pérez ◽  
José-Luis Vivancos ◽  
Carolina Aparicio-Fernández ◽  
Samuel A. Prieto
Keyword(s):  
Computer Vision ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Building Energy ◽  
Energy Monitoring ◽  
3D Computer Vision ◽  
Building Monitoring ◽  
Heritage Buildings ◽  
Heritage Building ◽  
Definition Of

The energy monitoring of heritage buildings has, to date, been governed by methodologies and standards that have been defined in terms of sensors that record scalar magnitudes and that are placed in specific positions in the scene, thus recording only some of the values sampled in that space. In this paper, however, we present an alternative to the aforementioned technologies in the form of new sensors based on 3D computer vision that are able to record dense thermal information in a three-dimensional space. These thermal computer vision-based technologies (3D-TCV) entail a revision and updating of the current building energy monitoring methodologies. This paper provides a detailed definition of the most significant aspects of this new extended methodology and presents a case study showing the potential of 3D-TCV techniques and how they may complement current techniques. The results obtained lead us to believe that 3D computer vision can provide the field of building monitoring with a decisive boost, particularly in the case of heritage buildings.

scholarly journals Three-Dimensional Computer Vision: A Geometric Viewpoint

1995 ◽  
Vol 38 (1) ◽  
pp. 85-86 ◽  
Author(s):  
P. K. Ghosh ◽  
S. P. Mudur
Keyword(s):  
Computer Vision ◽  
Three Dimensional

scholarly journals Waving plants and turbulent eddies

2010 ◽  
Vol 652 ◽  
pp. 1-4 ◽  
Author(s):  
J. J. FINNIGAN
Keyword(s):  
Three Dimensional ◽  
Large Eddy Simulations ◽  
Plant Canopy ◽  
Turbulent Eddies ◽  
Plant Canopies ◽  
Large Eddy ◽  
Flexible Plant ◽  
Paradoxical Results

New large-eddy simulations of flow over a flexible plant canopy by Dupont et al. (J. Fluid Mech., 2010, this issue, vol. 652, pp. 5–44) have produced apparently paradoxical results. Work over the last three decades had suggested that turbulent eddies could ‘lock onto’ to the waving frequency of uniform cereal canopies. Their new simulations contradict this view, although a resolution may lie in the essentially three-dimensional nature of the instability process that generates the dominant eddies above plant canopies.

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