152 research outputs found
A collaborative perspective in green construction risk management
No full textMany risks existing in the supply chain of green construction projects are poorly managed by traditional non-collaborative approaches leading to problems such as higher prices, inappropriate indoor environment quality, technological failures and legal battles that in turn adversely affect all stakeholders. To reduce the cases of failure in the green construction industry, it is necessary for supply chain (SC) key players to collaboratively identify, analyse and treat risks, considering benefits and concerns of all stakeholders inside the network.
This paper presents a method for collaborative risk management to provide informed advice to supply chain stakeholders to manage risks in the green construction industry. Contribution of the proposed collaborative approach is illustrated in a case study carried out in a green construction development project in Melbourne, Australia. The case study introduced in this research is sufficiently robust to provide evidence that collaborative approaches can add value to traditional methods of risk management and presents a modelling and analysis framework for assessing supply chain risks in the green construction.
Authors: Mehrdad Arashpour and Mohammadreza Arashpour, School of Property, Construction and Project Management, RMIT University.
First published in Kamardeen, I, Newton, S, Lim, B and Loosemore, M (ed.) Proceedings of the 37th Annual Conference of the Australasian Universities Building Educators Association (AUBEA), Sydney, Australia, 4th - 6th July 2012, pp. 1-11
RabbitStamp Test Sequence
No full text# RabbitStamp sequence by LISA ULB
The test sequence "RabbitStamp" is provided by Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.
# License:
CC BY-NC-SA
ONLY Available for Academic Usage
# Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "RabbitStamp Test Sequence", 2021.
@misc{fachada_RabbitStamp_2021,
title = {{RabbitStamp} {Test} {Sequence}},
author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },
month = jul,
year = {2021},
doi = {10.5281/zenodo.5053771}
}
[2] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[DLF] Plenoptic 2.0 Multiview Lenslet Dataset and Preliminary Experiments [m56429]", 2021.
@article{fachada_RabbitStamp_2021,
title = {[DLF] {Plenoptic} 2.0 {Multiview} {Lenslet} {Dataset} and {Preliminary} {Experiments} [m56429]},
author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },
journal = {ISO/IEC JTC1/SC29/WG11},
month = apr,
year = {2021}
}
[3] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[LVC] Update for RabbitStamp: Plenoptic 2.0 Multiview Lenslet Dataset [m57100]", 2021.
@article{fachada_RabbitStamp_2021,
title = {[LVC] {Update} for {RabbitStamp}: {Plenoptic} 2.0 {Multiview} {Dataset} [m56429]},
author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },
journal = {ISO/IEC JTC1/SC29/WG11},
month = jul,
year = {2021}
}
[4] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[LVC] Exploration Experiments using RabbitStamp Multiview Lenslet Images [m57101]", 2021.
@article{fachada_RabbitStamp_2021,
title = {[LVC] {Exploration} {Experiments} {Using} {RabbitStamp} {Multiview} {Lenslet} {Images} [m56429]},
author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad},
journal = {ISO/IEC JTC1/SC29/WG11},
month = jul,
year = {2021}
}
# Production:
Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Universite Libre de Bruxelles, Belgium.
# Content:
This dataset contains a test scene acquired with a raytrix camera [1] array of 7x3 views. For details of the dataset, please refer to the references mentioned above.
The dataset contains:
- a `depth_7x3_center` depth maps computed with DERS reference software [2] in yuv40016ble format and json configuration files to do so,
- a `multiview_7x3_5x5_images` Calibrated subimages computed with RLC [3] in yuv42010ble format, the cameras.json with the camera parameters and view_synthesis.json with the view synthesis experiment.
- a `multiview_7x3_lenslets` folder containing the lenslet views in yuv42010ble format, the Raytrix xml calibration file and RLC cfg file for conversion to multiview.
# References and links:
[1] Raytrix, https://raytrix.de/
[2] S. Rogge and D. Bonatto and J. Sancho and R. Salvador and E. Juarez and A. Munteanu and G. Lafruit, "MPEG-I Depth Estimation Reference Software", in 2019 International Conference on 3D Immersion (IC3D), 2019.
[3] M. Teratani and T. Fujii, "[MPEG-I Visual] Conversion of Lenslet Data Capture by Single Focussed Plenoptic Camera to Multiview Video using RLC0.3 [N18567]", ISO/IEC JTC1/SC29/WG11, 201
Transparent Magritte Test Sequence
No full textTransparent-Magritte sequence by LISA ULB
The test sequence "Transparent Magritte" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB, ULB.
License:
CC BY-NC-SA
Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Transparent Magritte Test Sequence", 2021.
@misc{fachada_transparent_2021,
title = {Transparent {Magritte} {Test} {Sequence}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
month = feb,
year = {2021},
doi = {10.5281/zenodo.4488243}
}
[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.
@inproceedings{fachada_light_2021,
title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},
booktitle = {Electronic {Imaging}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
year = {2021}
}
Production
Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.
Content:
This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Bblender file and the rendered scene.
The scene contains a transparent refractive torus rendered in a regular camera array of 21x21 cameras.
In addition to the 3D model, two folders are available:
- `centered_cameras` resolution of 1000x1000, the cameras are centered on the refractive torus.
- `parallel_cameras` resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.
Each of these folders contains:
- a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],
- a `parameters.cfg` generated with [2],
- a `texture` folder containing the rendered views in png format,
- a `depth` folder containing the associated depth maps in exr format.
References and links:
[1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.
[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,
https://github.com/lightfield-analysis/blender-addon
https://github.com/dbonattoj/blender-addon
[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.
https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-formatAcknoledgments:
This work was supported by
Les Fonds de la Recherche Scientifique - FNRS, Belgium, under Grant n°3679514$, ColibriH
The European Commision project n°951989 on Interactive Technologies, H2020-ICT-2019-3, Hovitron.
Sarah Fachada is a Research Fellow of the Fonds de la Recherche Scientifique - FNRS, Belgiu
Magritte Sphere
No full text# Magritte-Sphere sequence by LISA ULB
The test sequence "Magritte Sphere" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.
# License:
CC BY-NC-SA
# Terms of Use:
Anykind of publication or report using this sequence should refer to the following references.
[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Magritte Sphere Test Sequence", 2021.
@misc{fachada_magritte_2021,
title = {{Magritte} {Sphere} {Test} {Sequence}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
month = feb,
year = {2021},
doi = {10.5281/zenodo.5048265}
}
[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.
@inproceedings{fachada_light_2021,
title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},
booktitle = {Electronic {Imaging}},
author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},
year = {2021}
}
# Production:
Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.
# Content:
This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Blender file and the rendered scene.
The scene contains a non-Lambertian (transparent-refractive (T) or mirror-specular (M)) sphere rendered in a regular camera array of 21x21 cameras.
In addition to the 3D model, we provide the rendered images : resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.
The dataset contains:
- a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],
- a `parameters.cfg` generated with [2],
- a `texture_M` folder containing the rendered views in png format for the mirror object,
- a `texture_T` folder containing the rendered views in png format for the transparent object,
- a `mask` folder containing the mask indicating the sphere,
- a `depth` folder containing the associated depth maps in exr format.
# References and links:
[1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.
[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,
https://github.com/lightfield-analysis/blender-addon
https://github.com/dbonattoj/blender-addon
[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.
https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-formatAcknowledgments:
This work was supported by:
Les Fonds de la Recherche Scientifique - FNRS, Belgium, under Grant n°3679514$, ColibriH,
The European Commision project n°951989 on Interactive Technologies, H2020-ICT-2019-3, Hovitron.
Sarah Fachada is a Research Fellow of the Fonds de la Recherche Scientifique - FNRS, Belgiu
Nurses' readiness in research utilization: Moving toward
No full textNurses' readiness in research utilization: Moving toward evidence-based practice Mehrdad, N.1* (PhD); Salsali, M.2 (PhD); Kazemnejad, A.3 (PhD) 1. Assistant Professor, Dept. of Community Health Nursing, Faculty of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, Iran. *(Corresponding Author) e-mail: [email protected] 2. Professor, Faculty of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran.3. Professor, Dept. of Biostatistics, Tarbiat Modarress University, Tehran, Iran. Abstract Background and aimResearch utilization is a mechanism for transferring the results of research into practice and improving the quality of care in nursing. The aim of this study was to determine nurses’ readiness to utilize research needed for applying evidence-based practice. Materials and methodsIn this descriptive study, 375 nurses in all teaching hospitals affiliated with Tehran University of Medical Sciences were selected by stratified random sampling method. A 4-part questionnaire with open and close-ended questions including professional profile, research activities, research skills and access to research resources was used foe data collection. Content as well as face validities and Cronbach's α for reliability (0.82) were identified. Findings 85.9% of nurses had weak readiness in research utilization. Both research activities and skills were also low (71.4% and 82.7% respectively). 44% of nurses had insufficient access to research resources. A significant relationship was found between nurses' educational level, participation in research activities as well as English language skills and their readiness in research utilization. ConclusionLack of skills and inaccessibility to research findings lead to weak readiness for research utilization. With respect to the importance of utilizing research findings, organizational and administrative support, continuing education programs, well-defined processes and pathways to facilitate research utilization need to be provided for nurses. Keywords: Research utilization, Evidence-based practice, Nurses. *Corresponding Author: Neda Mehrdad. Assistant Professor, Dept. of Community Health Nursing, Faculty of Nursing and Midwifery, Iran University of Medical Sciences, Tehran, Iran. E-mail: [email protected] Effects of bladder irrigation with chl
Periodontal condition in rats with experimental diabetes mellitus after orthodontic surgery
No full textAzari Mehrdad Mohammad Ali. Periodontal condition in rats with experimental diabetes mellitus after orthodontic surgery. Journal of Education, Health and Sport. 2019;9(10):246-252. eISSN 2391-8306. DOI http://dx.doi.org/10.5281/zenodo.3522313
http://ojs.ukw.edu.pl/index.php/johs/article/view/7610
The journal has had 7 points in Ministry of Science and Higher Education parametric evaluation. Part B item 1223 (26/01/2017).
1223 Journal of Education, Health and Sport eISSN 2391-8306 7
© The Authors 2019;
This article is published with open access at Licensee Open Journal Systems of Kazimierz Wielki University in Bydgoszcz, Poland
Open Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author (s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non commercial license Share alike.
(http://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Received: 03.10.2019. Revised: 08.10.2019. Accepted: 29.10.2019.
UDK 616.31:615.37:616.179:379:008
PERIODONTAL CONDITION IN RATS WITH EXPERIMENTAL DIABETES MELLITUS AFTER ORTHODONTIC SURGERY
Azari Mehrdad Mohammad Ali
Odessa National Medical University
[email protected]
Abstract
Background. Determine periodontal condition in rats with experimental diabetes mellitus after orthodontic surgery.
Methods. In rats, type 1 diabetes mellitus (DM1) was reproduced with aloxane (100 mg / kg, intraperitoneally) once. Orthodontic surgery was performed by fixing the spring, starting from the 12th day. Animal euthanasia was performed on the 35th day of the experiment. The activity of urease, lysozyme, catalase, elastase, as well as the content of malondialdehyde (MDA) and hyaluronic acid were determined in the gum homogenate. In the alveolar bone homogenate, the activity of alkaline phosphatase (AlP) and acid phosphatase (AcP) and elastase was determined, as well as the content of calcium and protein. The antioxidant-prooxidant index (API) was calculated by the ratio of catalase activity and MDA content, and the degree of dysbiosis according to A. P. Levitsky was calculated by the ratio of the relative activities of urease and lysozyme. The mineralizing activity (MA) was calculated by the ratio of the activity of alkaline phosphatase (AlP) and AcP in the bone tissue and the mineralization (MD) degree was calculated by the ratio of the concentration of calcium, and protein.
Results. In rats with DM1, the level of elastase, urease, MDA and the degree of dysbiosis increase in the gum, however, the level of lysozyme, hyaluronic acid, and the API index decrease. In the bone tissue of the periodontium of rats with type 1 diabetes, the level of alkaline phosphatase and MA decreases, but the level of AC increases. Orthodontic surgery significantly reduces the degree of dysbiosis in the gums and shows a tendency to increase API and decrease elastase activity. After orthodontic surgery, rats significantly increase the level of alkaline phosphatase and MA.
Conclusion. With type 1 diabetes, periodontitis, dysbiosis develops and the mineralizing activity of periodontal bone tissue decreases. Orthodontic surgery tends to improve periodontal conditions.
Keywords: periodontium; diabetes mellitus; orthodontics; dysbiosis; inflammation; mineralizing activity
Autonomous Approach and Landing Algorithms for Unmanned Aerial Vehicles
No full textIn recent years, several research activities have been developed in order to increase the autonomy features in Unmanned Aerial Vehicles (UAVs), to substitute human pilots in dangerous missions or simply in order to execute specific tasks more efficiently and cheaply. In particular, a significant research effort has been devoted to achieve high automation in the landing phase, so as to allow the landing of an aircraft without human intervention, also in presence of severe environmental disturbances. The worldwide research community agrees with the opportunity of the dual use of UAVs (for both military and civil purposes), for this reason it is very important to make the UAVs and their autolanding systems compliant with the actual and future rules and with the procedures regarding autonomous flight in ATM (Air Traffic Management) airspace in addition to the typical military aims of minimizing fuel, space or other important parameters during each autonomous task.
Developing autolanding systems with a desired level of reliability, accuracy and safety involves an evolution of all the subsystems related to the guide, navigation and control disciplines. The main drawbacks of the autolanding systems available at the state of art concern or the lack of adaptivity of the trajectory generation and tracking to unpredicted external events, such as varied environmental condition and unexpected threats to avoid, or the missed compliance with the guide lines imposed by certification authorities of the proposed technologies used to get the desired above mentioned adaptivity.
During his PhD period the author contributed to the development of an autonomous approach and landing system considering all the indispensable functionalities like: mission automation logic, runway data managing, sensor fusion for optimal estimation of vehicle state, trajectory generation and tracking considering optimality criteria, health management algorithms.
In particular the system addressed in this thesis is capable to perform a fully adaptive autonomous landing starting from any point of the three dimensional space. The main novel feature of this algorithm is that it generates on line, with a desired updating rate or at a specified event, the nominal trajectory for the aircraft, based on the actual state of the vehicle and on the desired state at touch down point. Main features of the autolanding system based on the implementation of the proposed algorithm are: on line trajectory re-planning in the landing phase, fully autonomy from remote pilot inputs, weakly instrumented landing runway (without ILS availability), ability to land starting from any point in the space and autonomous management of failures and/or adverse atmospheric conditions, decision-making logic evaluation for key-decisions regarding possible execution of altitude recovery manoeuvre based on the Differential GPS integrity signal and compatible with the functionalities made available by the future GNSS system.
All the algorithms developed allow reducing computational tractability of trajectory generation and tracking problems so as to be suitable for real time implementation and to still obtain a feasible (for the vehicle) robust and adaptive trajectory for the UAV.
All the activities related to the current study have been conducted at CIRA (Italian Aerospace Research Center) in the framework of the aeronautical TECVOL project whose aim is to develop innovative technologies for the autonomous flight. The autolanding system was developed by the TECVOL team and the author’s contribution to it will be outlined in the thesis.
Effectiveness of proposed algorithms has been then evaluated in real flight experiments, using the aeronautical flying demonstrator available at CIRA
ComplexObjectMoveLinear Cam
No full text<h1>ComplexObjectMoveLinearCam:</h1>
<p>This plenoptic sequence dataset "ComplexObjectMoveLinearCam" is provided by Eline Soetens, Gauthier Lafruit, Mehrdad Teratani members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium and Jonghoon Yim, Byeungwoo Jeon members of the Department of Electrical and Computer Engineering, SKKU (Sungkyunkwan University), Korea.</p>
<h1>License:</h1>
<p>CC BY-NC-SA</p>
<h1>Terms of Use:</h1>
<p>Any kind of publication or report using this sequence should refer to the following reference :</p>
<p>Eline Soetens, Jonghoon Yim, Byeungwoo Jeon, Gauthier Lafruit, Mehrdad Teratani, "ComplexObjectMoveLinearCam", 2024.</p>
<p>@misc{soetens_ComplexObjectMoveLinearCam_2024, title = {ComplexObjectMoveLinearCam}}, author = {Soetens, Eline and Yim, Jonghoon and Jeon, Byeungwoo and Lafruit, Gauthier and Teratani, Mehrdad }, month = jan, year = {2024}, doi = {10.5281/zenodo.10571134} }</p>
<h1>Content:</h1>
<p>This sequence was captured using a Raytrix R8 plenoptic camera [1] equipped with a 35mm lens. The sequence was recorded for 10 seconds at 30 fps with the Raytrix software RxLive 5.0 [2]. Each frame has a 3840 x 2160 resolution. The scene contains blocks with a simple texture, a sphere with a complex texture, two toy cars, and a reflective unicorn statue placed on a moving board. The toy cars and the unicorn statue present a lot of specularities, thus both objects have a non-Lambertian texture. The scene was filmed by moving the camera in a linear motion from left to right.</p>
<p>Please find a detailed description of the content of each file below:</p>
<ul>
<li><code>ComplexObjectMoveLinearCam</code>: Contains the 300 frames of the sequences in PNG format.</li>
<li><code>R8-ComplexObjectMoveLinearCam_Calibration.xml</code>: Contains the calibration parameters of the plenoptic camera given by the Raytrix SDK.</li>
</ul>
<h1>References and links:</h1>
<p>[1] <a href="https://raytrix.de/products/">https://raytrix.de/products/</a></p>
<p>[2] <a href="https://raytrix.de/downloads/">https://raytrix.de/downloads/</a></p><p>This work was supported in part by the FER 2021 project (1060H000066-FAISAN), Belgium; in part by the Emile DEFAY 2021 project (4R00H000236), Belgium; and in part by the FER 2023 project (1060H000075), Belgium. This work was supported in part by Basic Science Research Program (RS-2023-00208453) through the National Research Foundation of Korea (NRF) and by the ICT Creative Consilience Program (IITP-2023-2020-0-01821) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation), both funded by the Ministry of Science and ICT, Korea.</p>
ComplexObjectMoveRandomCam
No full text<h1>ComplexObjectMoveRandomCam:</h1>
<p>This plenoptic sequence dataset "ComplexObjectMoveRandomCam" is provided by Eline Soetens, Gauthier Lafruit, Mehrdad Teratani members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium and Jonghoon Yim, Byeungwoo Jeon members of the Department of Electrical and Computer Engineering, SKKU (Sungkyunkwan University), Korea.</p>
<h1>License:</h1>
<p>CC BY-NC-SA</p>
<h1>Terms of Use:</h1>
<p>Any kind of publication or report using this sequence should refer to the following reference :</p>
<p>Eline Soetens, Jonghoon Yim, Byeungwoo Jeon, Gauthier Lafruit, Mehrdad Teratani, "ComplexObjectMoveRandomCam", 2024.</p>
<p>@misc{soetens_ComplexObjectMoveRandomCam_2024, title = {ComplexObjectMoveRandomCam}}, author = {Soetens, Eline and Yim, Jonghoon and Jeon, Byeungwoo and Lafruit, Gauthier and Teratani, Mehrdad }, month = jan, year = {2024}, doi = {10.5281/zenodo.10571393} }</p>
<h1>Content:</h1>
<p>This sequence was captured using a Raytrix R8 plenoptic camera [1] equipped with a 35mm lens. The sequence was recorded for 10 seconds at 30 fps with the Raytrix software RxLive 5.0 [2]. Each frame has a 3840 x 2160 resolution. The scene contains blocks with a simple texture, a sphere with a complex texture, two toy cars, and a reflective unicorn statue placed on a moving board. The toy cars and the unicorn statue present a lot of specularities, thus both objects have a non-Lambertian texture. The scene was filmed by moving the camera by hand in a random motion.</p>
<p>Please find a detailed description of the content of each file below:</p>
<ul>
<li><code>ComplexObjectMoveRandomCam</code>: Contains the 300 frames of the sequences in PNG format.</li>
<li><code>R8-ComplexObjectMoveRandomCam_Calibration.xml</code>: Contains the calibration parameters of the plenoptic camera given by the Raytrix SDK.</li>
</ul>
<h1>References and links:</h1>
<p>[1] <a href="https://raytrix.de/products/">https://raytrix.de/products/</a></p>
<p>[2] <a href="https://raytrix.de/downloads/">https://raytrix.de/downloads/</a></p><p>This work was supported in part by the FER 2021 project (1060H000066-FAISAN), Belgium; in part by the Emile DEFAY 2021 project (4R00H000236), Belgium; and in part by the FER 2023 project (1060H000075), Belgium. This work was supported in part by Basic Science Research Program (RS-2023-00208453) through the National Research Foundation of Korea (NRF) and by the ICT Creative Consilience Program (IITP-2023-2020-0-01821) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation), both funded by the Ministry of Science and ICT, Korea. </p>
ComplexObjectMove
No full text<p><strong>ComplexObjectMove:</strong></p>
<p>This plenoptic sequence dataset "ComplexObjectMove" is provided by Eline Soetens, Gauthier Lafruit, Mehrdad Teratani members of LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium and Jonghoon Yim, Byeungwoo Jeon members of the Departement of Electrical and Computer Engineering, SKKU (Sungkyunkwan University), Korea.</p>
<p><strong>License:</strong></p>
<p>CC BY-NC-SA</p>
<p><strong>Terms of Use:</strong></p>
<p>Any kind of publication or report using this sequence should refer to the following reference :</p>
<p>Eline Soetens, Jonghoon Yim, Byeungwoo Jeon, Gauthier Lafruit, Mehrdad Teratani, "ComplexObjectMove", 2024.</p>
<p>@misc{soetens_ComplexObjectMove_2024, title = {ComplexObjectMove}}, author = {Soetens, Eline and Yim, Jonghoon and Jeon, Byeungwoo and Lafruit, Gauthier and Teratani, Mehrdad }, month = jan, year = {2024}, doi = {10.5281/zenodo.10569052} }</p>
<p><strong>Content:</strong></p>
<p>This sequence was captured using a Raytrix R8 plenoptic camera [1] equipped with a 35mm lens. The sequence was recorded for 10 seconds at 30 fps with the Raytrix software RxLive 5.0 [2]. Each frame has a 3840 x 2160 resolution. The scene contains blocks with a simple texture, a sphere with a complex texture, two toy cars, and a reflective unicorn statue placed on a moving board. The toy cars and the unicorn statue present a lot of specularities, thus both objects have a non-Lambertian texture. The camera is fixed and only the objects are moving in the scene.</p>
<p>Please find a detailed description of the content of each file below:</p>
<ul>
<li><code>ComplexObjectMove</code>: Contains the 300 frames of the sequences in PNG format.</li>
<li><code>R8-ComplexObjectMove_Calibration.xml</code>: Contains the calibration parameters of the plenoptic camera given by the Raytrix SDK.</li>
</ul>
<p><strong>Reference and links:</strong></p>
<p>[1] <a href="https://raytrix.de/products/" target="_blank" rel="noopener">https://raytrix.de/products/</a></p>
<p>[2] <a href="https://raytrix.de/downloads/" target="_blank" rel="noopener">https://raytrix.de/downloads/</a></p><p>This work was supported in part by the FER 2021 project (1060H000066-FAISAN), Belgium; in part by the Emile DEFAY 2021 project (4R00H000236), Belgium; and in part by the FER 2023 project (1060H000075), Belgium. This work was supported in part by Basic Science Research Program (RS-2023-00208453) through the National Research Foundation of Korea (NRF) and by the ICT Creative Consilience Program (IITP-2023-2020-0-01821) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation), both funded by the Ministry of Science and ICT, Korea.</p>
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