scholarly journals Joint Roughness Profiling using Photogrammetry

2021 ◽  
Author(s):  
T. SARICAM ◽  
Hasan Ozturk
Keyword(s):  
Low Cost ◽  
Absolute Error ◽  
Depth Of Field ◽  
Roughness Coefficient ◽  
Image Size ◽  
Distance Ratio ◽  
Joint Roughness ◽  
Depth Maps ◽  
Stereo Photogrammetry ◽  
Disparity Maps

Abstract We propose an automated camera setup for photogrammetric roughness analysis in the laboratory environment. The developed fast and low-cost automation setup can be very useful for tedious and laborsome manual field logging practices. The photographs are processed in MATLAB to obtain disparity maps. Coding routines for stereo photogrammetry and digital measurements are written in MATLAB. Secondly, 6 effecting factors (projecting an image onto core face, depth of field, brightness, camera-to-object to baseline distance ratio, projected image size and occlusion) influencing noise in roughness depth maps computed by employing stereo photogrammetry are investigated. After deciding the best values that allow the lowest amount of noise, depth maps of 6 core faces are computed. Using the 3D point cloud generated, roughness profile measurements are made. Then, 8 profile measurements are made for each core face, both manually and digitally. The accuracy of the disparity maps has been verified by comparing 48 joint roughness coefficient (JRC) measurements made manually using a profile gauge. It was proved that surface roughness can be measured very fast in millimetric accuracy with an average Root Mean Square Error (RMSE) of 3.50 and Mean Absolute Error (MAE) of 3.02 by the help of the proposed set-up and calibration.

Optimal Camera Configuration for 3D Scanning of Human Hand

2019 ◽  
Author(s):  
Yusheng Yang ◽  
Willemijn S. Elkhuizen ◽  
Tao Hou ◽  
Tessa T. W. Essers ◽  
Yu Song
Keyword(s):  
3D Reconstruction ◽  
Spatial Configuration ◽  
Low Cost ◽  
Absolute Error ◽  
Depth Of Field ◽  
Human Hand ◽  
Spatial Configurations ◽  
Close Range ◽  
3D Printed ◽  
3D Scans

Abstract Fast, accurate and low-cost 3D scans are the key in designing personalized products. In this paper, using close-range photogrammetry technique, we aim at finding the “just enough” number of cameras and their spatial configurations for a full 3D reconstruction of the human hand. Given an object, we establish a mathematical model to describe the 3D constructible ratio based on the field of the view and the depth of field of each camera, as well as the visibility of each part of the object in the view of each camera. Furthermore, we introduce spatial constrains to arrange cameras along two rings for: 1) solving the problem of the large number of parameters in the unconstrained optimization, and 2) the feasibility and flexibility in the construction. Based on the found number of cameras and the spatial configuration of each camera, a prototype scanner was built to verify the effectiveness of the proposed method. The mean absolute error between the 3D scan of a 3D printed hand and its original CAD model was found to be 0.38mm, which is smaller than that (0.52mm) of using the conventional setup. Besides, the distribution of errors is smaller as well, which implicates a better full 3D reconstruction of the scanned hand.

scholarly journals A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis

2021 ◽  
Vol 11 (4) ◽  
pp. 1667
Author(s):  
Kerstin Klaser ◽  
Pedro Borges ◽  
Richard Shaw ◽  
Marta Ranzini ◽  
Marc Modat ◽  
...  
Keyword(s):  
Medical Image Analysis ◽  
Absolute Error ◽  
Magnetic Resonance Images ◽  
The Body ◽  
Whole Body ◽  
Image Size ◽  
Body Images ◽  
Image Translation ◽  
The Mean ◽  
Synthetic Ct

Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.

scholarly journals An On-Line Low-Cost Irradiance Monitoring Network with Sub-Second Sampling Adapted to Small-Scale PV Systems

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3405 ◽  
Author(s):  
Manuel Espinosa-Gavira ◽  
Agustín Agüera-Pérez ◽  
Juan González de la Rosa ◽  
José Palomares-Salas ◽  
José Sierra-Fernández
Keyword(s):  
Sensor Networks ◽  
Low Cost ◽  
Monitoring Network ◽  
Absolute Error ◽  
Small Scale ◽  
Photovoltaic Systems ◽  
Real Time Control ◽  
Short Term ◽  
Time Control ◽  
Cloud Motion

Very short-term solar forecasts are gaining interest for their application on real-time control of photovoltaic systems. These forecasts are intimately related to the cloud motion that produce variations of the irradiance field on scales of seconds and meters, thus particularly impacting in small photovoltaic systems. Very short-term forecast models must be supported by updated information of the local irradiance field, and solar sensor networks are positioning as the more direct way to obtain these data. The development of solar sensor networks adapted to small-scale systems as microgrids is subject to specific requirements: high updating frequency, high density of measurement points and low investment. This paper proposes a wireless sensor network able to provide snapshots of the irradiance field with an updating frequency of 2 Hz. The network comprised 16 motes regularly distributed over an area of 15 m × 15 m (4 motes × 4 motes, minimum intersensor distance of 5 m). The irradiance values were estimated from illuminance measurements acquired by lux-meters in the network motes. The estimated irradiances were validated with measurements of a secondary standard pyranometer obtaining a mean absolute error of 24.4 W/m 2 and a standard deviation of 36.1 W/m 2 . The network was able to capture the cloud motion and the main features of the irradiance field even with the reduced dimensions of the monitoring area. These results and the low-cost of the measurement devices indicate that this concept of solar sensor networks would be appropriate not only for photovoltaic plants in the range of MW, but also for smaller systems such as the ones installed in microgrids.

scholarly journals A New Method for Determination of Joint Roughness Coefficient of Rock Joints

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Shigui Du ◽  
Huicai Gao ◽  
Yunjin Hu ◽  
Man Huang ◽  
Hua Zhao
Keyword(s):  
Scale Effect ◽  
Fractal Model ◽  
Rock Joints ◽  
Effective Length ◽  
Roughness Coefficient ◽  
Joint Roughness Coefficient ◽  
Joint Roughness ◽  
Exposed Rock ◽  
Study Results

The joint roughness coefficient (JRC) of rock joints has the characteristic of scale effect. JRC measured on small-size exposed rock joints should be evaluated by JRC scale effect in order to obtain the JRC of actual-scale rock joints, since field rock joints are hardly fully exposed or well saved. Based on the validity analysis of JRC scale effect, concepts of rate of JRC scale effect and effective length of JRC scale effect were proposed. Then, a graphic method for determination of the effective length of JRC scale effect was established. Study results show that the JRC of actual-scale rock joints can be obtained through a fractal model of JRC scale effect according to the statistically measured results of the JRC of small-size partial exposed rock joints and by the selection of fractal dimension of JRC scale effect and the determination of effective length of JRC scale effect.

scholarly journals A MEDIAN-BASED DEPTHMAP FUSION STRATEGY FOR THE GENERATION OF ORIENTED POINTS

2016 ◽  
Vol III-3 ◽  
pp. 115-122
Author(s):  
M. Rothermel ◽  
N. Haala ◽  
D. Fritsch
Keyword(s):  
Surface Reconstruction ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Depth Map ◽  
Median Filtering ◽  
Tree Structures ◽  
Fusion Strategy ◽  
Depth Maps ◽  
Disparity Maps ◽  
Nearest Neighbor Queries

Due to good scalability, systems for image-based dense surface reconstruction often employ stereo or multi-baseline stereo methods. These types of algorithms represent the scene by a set of depth or disparity maps which eventually have to be fused to extract a consistent, non-redundant surface representation. Generally the single depth observations across the maps possess variances in quality. Within the fusion process not only preservation of precision and detail but also density and robustness with respect to outliers are desirable. Being prune to outliers, in this article we propose a local median-based algorithm for the fusion of depth maps eventually representing the scene as a set of oriented points. Paying respect to scalability, points induced by each of the available depth maps are streamed to cubic tiles which then can be filtered in parallel. Arguing that the triangulation uncertainty is larger in the direction of image rays we define these rays as the main filter direction. Within an additional strategy we define the surface normals as the principle direction for median filtering/integration. The presented approach is straight-forward to implement since employing standard oc- and kd-tree structures enhanced by nearest neighbor queries optimized for cylindrical neighborhoods. We show that the presented method in combination with the MVS (Rothermel et al., 2012) produces surfaces comparable to the results of the Middlebury MVS benchmark and favorably compares to an state-of-the-art algorithm employing the Fountain dataset (Strecha et al., 2008). Moreover, we demonstrate its capability of depth map fusion for city scale reconstructions derived from large frame airborne imagery.

scholarly journals Arduino based platform for process control learning

2020 ◽  
Vol 6 (5) ◽  
pp. 0585-0593
Author(s):  
Bruna Couto Molinar Henrique ◽  
Leonardo Couto Molinar Henrique ◽  
Humberto Molinar Henrique
Keyword(s):  
Open Source ◽  
Real Time ◽  
Low Cost ◽  
Control Unit ◽  
Absolute Error ◽  
Internal Model Control ◽  
Pid Controllers ◽  
Experimental Unit ◽  
Source Codes ◽  
Model Control

This work deals with implementation of an experimental flowrate control unit using free and low-cost hardware and software. The open-source software Processing was used to develop the source codes and user graphical interface and the open-source electronic prototyping platform Arduino was used to acquire data from an experimental unit. Work presents descriptions of the experimental setup, the real-time PID controllers used and theoretical/conceptual issues of Arduino. PID controllers based on internal model control, minimization of the integral of time-weighted absolute error, Ziegler-Nichols, and others were tuned for setpoint and load changes and real-time runs were carried out in order to make real-time use of  control theory learned in academy. Results showed the developed platform proved to be suitable for use in experimental setups allowing users compare their ideas and expectations with the experimental evidence in a real and low-cost fashion. In addition, the instrumentation is simple to configure with acceptable level noise and particularly useful for control/automation learning with educational purposes.

scholarly journals Autonomous Navigation of a Center-Articulated and Hydrostatic Transmission Rover using a Modified Pure Pursuit Algorithm in a Cotton Field

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4412
Author(s):  
Kadeghe Fue ◽  
Wesley Porter ◽  
Edward Barnes ◽  
Changying Li ◽  
Glen Rains
Keyword(s):  
Autonomous Navigation ◽  
Low Cost ◽  
Path Following ◽  
Absolute Error ◽  
Satellite System ◽  
Measurement Unit ◽  
Cotton Field ◽  
Cotton Production ◽  
Hydrostatic Transmission ◽  
Pure Pursuit

This study proposes an algorithm that controls an autonomous, multi-purpose, center-articulated hydrostatic transmission rover to navigate along crop rows. This multi-purpose rover (MPR) is being developed to harvest undefoliated cotton to expand the harvest window to up to 50 days. The rover would harvest cotton in teams by performing several passes as the bolls become ready to harvest. We propose that a small robot could make cotton production more profitable for farmers and more accessible to owners of smaller plots of land who cannot afford large tractors and harvesting equipment. The rover was localized with a low-cost Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS), encoders, and Inertial Measurement Unit (IMU)s for heading. Robot Operating System (ROS)-based software was developed to harness the sensor information, localize the rover, and execute path following controls. To test the localization and modified pure-pursuit path-following controls, first, GNSS waypoints were obtained by manually steering the rover over the rows followed by the rover autonomously driving over the rows. The results showed that the robot achieved a mean absolute error (MAE) of 0.04 m, 0.06 m, and 0.09 m for the first, second and third passes of the experiment, respectively. The robot achieved an MAE of 0.06 m. When turning at the end of the row, the MAE from the RTK-GNSS-generated path was 0.24 m. The turning errors were acceptable for the open field at the end of the row. Errors while driving down the row did damage the plants by moving close to the plants’ stems, and these errors likely would not impede operations designed for the MPR. Therefore, the designed rover and control algorithms are good and can be used for cotton harvesting operations.

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