scholarly journals Scale-Aware Multi-View Reconstruction Using an Active Triple-Camera System

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6726
Author(s):  
Hang Luo ◽  
Christian Pape ◽  
Eduard Reithmeier
Keyword(s):  
Measurement System ◽  
3D Modeling ◽  
Reconstruction Method ◽  
Single Shot ◽  
Camera System ◽  
Photographic Evidence ◽  
Outdoor Environments ◽  
Processing Module ◽  
Synthetic Datasets ◽  
Fusion Scheme

This paper presents an active wide-baseline triple-camera measurement system designed especially for 3D modeling in general outdoor environments, as well as a novel parallel surface refinement algorithm within the multi-view stereo (MVS) framework. Firstly, the pre-processing module converts the synchronized raw triple images from one single-shot acquisition of our setup to aligned RGB-Depth frames, which are then used for camera pose estimation using iterative closest point (ICP) and RANSAC perspective-n-point (PnP) approaches. Afterwards, an efficient dense reconstruction method, mostly implemented on the GPU in a grid manner, takes the raw depth data as input and optimizes the per-pixel depth values based on the multi-view photographic evidence, surface curvature and depth priors. Through a basic fusion scheme, an accurate and complete 3D model can be obtained from these enhanced depth maps. For a comprehensive test, the proposed MVS implementation is evaluated on benchmark and synthetic datasets, and a real-world reconstruction experiment is also conducted using our measurement system in an outdoor scenario. The results demonstrate that (1) our MVS method achieves very competitive performance in terms of modeling accuracy, surface completeness and noise reduction, given an input coarse geometry; and (2) despite some limitations, our triple-camera setup in combination with the proposed reconstruction routine, can be applied to some practical 3D modeling tasks operated in outdoor environments where conventional stereo or depth senors would normally suffer.

scholarly journals Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6659
Author(s):  
Aryuanto Soetedjo ◽  
Evy Hendriarianti
Keyword(s):  
Execution Time ◽  
Low Cost ◽  
Infrared Camera ◽  
Raspberry Pi ◽  
Camera System ◽  
Detection And Counting ◽  
Outdoor Environments ◽  
Static Images ◽  
Leaf Detection ◽  
Non Destructive

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

scholarly journals A Real-Time 3D Measurement System for the Blast Furnace Burden Surface Using High-Temperature Industrial Endoscope

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 869 ◽  
Author(s):  
Tianxiang Xu ◽  
Zhipeng Chen ◽  
Zhaohui Jiang ◽  
Jiancai Huang ◽  
Weihua Gui
Keyword(s):  
Blast Furnace ◽  
High Temperature ◽  
Real Time ◽  
Measurement System ◽  
Reconstruction Method ◽  
3D Measurement ◽  
3D Shape ◽  
Camera Array ◽  
3D Measurement System ◽  
High Dust

Capturing the three-dimensional (3D) shape of the burden surface of a blast furnace (BF) in real-time with high accuracy is crucial for improving gas flow distribution, optimizing coke operation, and stabilizing BF operation. However, it is difficult to perform 3D shape measurement of the burden surface in real-time during the ironmaking process because of the high-temperature, high-dust, and lightless enclosed environment inside the BF. To solve this problem, a real-time 3D measurement system is developed in this study by combining an industrial endoscope with a virtual multi-head camera array 3D reconstruction method. First, images of the original burden surface are captured using a purpose-built industrial endoscope. Second, a novel micro-pixel luminance polarization method is proposed and applied to compensate for the heavy noise in the backlit images due to high dust levels and poor light in the enclosed environment. Third, to extract depth information, a multifeature-based depth key frame classifier is designed to filter out images with high levels of clarity and displacement. Finally, a 3D shape burden surface reconstruction method based on a virtual multi-head camera array is proposed for capturing the real-time 3D shape of the burden surface in an operational BF. The results of an industrial experiment illustrate that the proposed method can measure the 3D shape of the entire burden surface and provide reliable burden surface shape information for BF control.

scholarly journals Optical Properties of Continuous and Pixelated Scintillation Crystals

2020 ◽  
Vol 16 ◽  
pp. 231
Author(s):  
M. Mikeli ◽  
... Et al.
Keyword(s):  
Reconstruction Method ◽  
Small Field ◽  
Photon Distribution ◽  
Camera System ◽  
Charge Center ◽  
Scintillation Crystals ◽  
Planar Images ◽  
Position Sensitive ◽  
Position Reconstruction ◽  
Position Sensitive Photomultiplier Tube

The optical photon distribution produced inside continuous and pixelated scintillation crystals by the absorption of a γ-ray have been studied with the photon transport program DETECT 2000. With this program the charge signals recorded by a multi-wired anode system, like the Position Sensitive PhotoMultiplier Tube (PSPMT) of a γ-Camera, are simulated. Based on the analytical parametrization which is fitted to experimental data, a new position reconstruction method for PSPMTs is proposed in this work. Planar images have been reconstructed with the new method and compared to the traditional charge center of gravity technique. Data are obtained from a small field, high resolution γ-Camera system with a multi- wired crossed anode using the R2486 (HAMAMATSU) PSPMT. Systematic studies for continuous and pixelated inorganic scintillation crystals of CsI(Tl) have been performed for different phantom geometries using small capillaries of 99mTc. The developed method seems to drastically improve the resolution of the reconstructed planar information, even when homogeneous crystals are used.

Complete 3D Modeling from Rotational Devices

2011 ◽  
Vol 317-319 ◽  
pp. 843-846
Author(s):  
Sheng Yong Chen ◽  
Da Wei Liu ◽  
Xiao Yan Wang ◽  
Wei Huang ◽  
Qiu Guan
Keyword(s):  
Linear Transformation ◽  
3D Modeling ◽  
3D Models ◽  
Image Sequences ◽  
Reconstruction Method ◽  
Projective Reconstruction ◽  
Complete Model ◽  
The Common

For acquisition of complete 3D models, this paper uses a rotational device to capture a set of image sequences. A direct projective reconstruction method is proposed by linear transformation, which can avoid getting corresponding points in more than two images. Actually, projective reconstructions are obtained from two neighboring images and the reconstructions are combined with the common 3D points. Finally, all reconstructions are merged into the initial one to construct a complete model. Several practical experiments have been carried out to validate the accuracy of the method.

Measurement of Electrochemical Machining Initial Gap Based on Machine Vision

2011 ◽  
Vol 230-232 ◽  
pp. 1190-1194 ◽  
Author(s):  
Min Kang ◽  
Hou Shang Li ◽  
Xiu Qing Fu
Keyword(s):  
Machine Vision ◽  
Measurement System ◽  
Measurement Method ◽  
Electrochemical Machining ◽  
Image Data ◽  
Ccd Camera ◽  
Camera System ◽  
Physical Size ◽  
System Calibration ◽  
Data Acquisition Card

In order to measure the initial gap between the workpiece and tool-cathode in electrochemical machining, the measurement method based on machine vision was studied in this paper. First, the measurement system based on machine vision was established. The hardware of the system consisted of CCD camera, image data acquisition card, light source and computer. The software of the system was developed by VC++6.0. Then, the original digital image of electrochemical machining initial gap collected by the CCD camera system was changed into the contour of image through graying, bivalency, edge detection and segmentation. Through system calibration, the physical size of the gap was calculated. Finally, relative experiments were carried out. The experimental results validated the feasibility of the method which measures the electrochemical machining initial gap based on machine vision.

scholarly journals Holographic optical field recovery using a regularized untrained deep decoder network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Farhad Niknam ◽  
Hamed Qazvini ◽  
Hamid Latifi
Keyword(s):  
Computational Imaging ◽  
Training Dataset ◽  
Reconstruction Method ◽  
Single Shot ◽  
Sample Type ◽  
Lack Of Information ◽  
Image Prior ◽  
Measured Information ◽  
Image Priors ◽  
Training Resources

AbstractImage reconstruction using minimal measured information has been a long-standing open problem in many computational imaging approaches, in particular in-line holography. Many solutions are devised based on compressive sensing (CS) techniques with handcrafted image priors or supervised deep neural networks (DNN). However, the limited performance of CS methods due to lack of information about the image priors and the requirement of an enormous amount of per-sample-type training resources for DNNs has posed new challenges over the primary problem. In this study, we propose a single-shot lensless in-line holographic reconstruction method using an untrained deep neural network which is incorporated with a physical image formation algorithm. We demonstrate that by modifying a deep decoder network with simple regularizers, a Gabor hologram can be inversely reconstructed via a minimization process that is constrained by a deep image prior. The outcoming model allows to accurately recover the phase and amplitude images without any training dataset, excess measurements, or specific assumptions about the object’s or the measurement’s characteristics.

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