scholarly journals DENSE 3D OBJECT RECONSTRUCTION USING STRUCTURED-LIGHT SCANNER AND DEEP LEARNING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 777-783
Author(s):  
V. V. Kniaz ◽  
V. A. Mizginov ◽  
L. V. Grodzitkiy ◽  
N. A. Fomin ◽  
V. A. Knyaz
Keyword(s):  
Optical Flow ◽  
3D Model ◽  
Stereo Matching ◽  
Structured Light ◽  
Primary Source ◽  
Ground Truth ◽  
Complex Surface ◽  
Systems Model ◽  
Structured Light Scanner ◽  
Starting Point

Abstract. Structured light scanners are intensively exploited in various applications such as non-destructive quality control at an assembly line, optical metrology, and cultural heritage documentation. While more than 20 companies develop commercially available structured light scanners, structured light technology accuracy has limitations for fast systems. Model surface discrepancies often present if the texture of the object has severe changes in brightness or reflective properties of its texture. The primary source of such discrepancies is errors in the stereo matching caused by complex surface texture. These errors result in ridge-like structures on the surface of the reconstructed 3D model. This paper is focused on the development of a deep neural network LineMatchGAN for error reduction in 3D models produced by a structured light scanner. We use the pix2pix model as a starting point for our research. The aim of our LineMatchGAN is a refinement of the rough optical flow A and generation of an error-free optical flow B̂. We collected a dataset (which we term ZebraScan) consisting of 500 samples to train our LineMatchGAN model. Each sample includes image sequences (Sl, Sr), ground-truth optical flow B and a ground-truth 3D model. We evaluate our LineMatchGAN on a test split of our ZebraScan dataset that includes 50 samples. The evaluation proves that our LineMatchGAN improves the stereo matching accuracy (optical flow end point error, EPE) from 0.05 pixels to 0.01 pixels.

scholarly journals 3D RECONSTRUCTION-REVERSE ENGINEERING – DIGITAL FABRICATION OF THE EGYPTIAN PALERMO STONE USING BY SMARTPHONE AND LIGHT STRUCTURED SCANNER

2018 ◽  
Vol XLII-2 ◽  
pp. 311-318 ◽  
Author(s):  
F. Di Paola ◽  
L. Inzerillo
Keyword(s):  
3D Reconstruction ◽  
Reverse Engineering ◽  
3D Model ◽  
Structured Light ◽  
Digital Fabrication ◽  
Black Stone ◽  
Structured Light Scanner ◽  
Dark Colour ◽  
Egyptian History ◽  
Texture Reconstruction

This paper presents a pipeline that has been developed to acquire a shape with particular features both under the geometric and radiometric aspects. In fact, the challenge was to build a 3D model of the black Stone of Palermo, where the oldest Egyptian history was printed with the use of hieroglyphs. The dark colour of the material and the superficiality of the hieroglyphs' groove have made the acquisition process very complex to the point of having to experiment with a pipeline that allows the structured light scanner not to lose the homologous points in the 3D alignment phase. For the texture reconstruction we used a last generation smartphone.

Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow

2021 ◽  
pp. 1-1
Author(s):  
Yong Deng ◽  
Jimin Xiao ◽  
Steven Zhiying Zhou ◽  
Jiashi Feng
Keyword(s):  
Optical Flow ◽  
Stereo Matching ◽  
Coarse To Fine

scholarly journals How does the robot feel? Perception of valence and arousal in emotional body language

2018 ◽  
Vol 9 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Mina Marmpena ◽  
Angelica Lim ◽  
Torbjørn S. Dahl
Keyword(s):  
Human Perception ◽  
Ground Truth ◽  
Body Language ◽  
Human Robot Interaction ◽  
Behavioral Experiments ◽  
Robot Interaction ◽  
Expression Of Emotion ◽  
Exploratory Approach ◽  
Starting Point ◽  
Valence And Arousal

Abstract Human-robot interaction in social robotics applications could be greatly enhanced by robotic behaviors that incorporate emotional body language. Using as our starting point a set of pre-designed, emotion conveying animations that have been created by professional animators for the Pepper robot, we seek to explore how humans perceive their affect content, and to increase their usability by annotating them with reliable labels of valence and arousal, in a continuous interval space. We conducted an experiment with 20 participants who were presented with the animations and rated them in the two-dimensional affect space. An inter-rater reliability analysis was applied to support the aggregation of the ratings for deriving the final labels. The set of emotional body language animations with the labels of valence and arousal is available and can potentially be useful to other researchers as a ground truth for behavioral experiments on robotic expression of emotion, or for the automatic selection of robotic emotional behaviors with respect to valence and arousal. To further utilize the data we collected, we analyzed it with an exploratory approach and we present some interesting trends with regard to the human perception of Pepper’s emotional body language, that might be worth further investigation.

Virtual skeletons: using a structured light scanner to create a 3D faunal comparative collection

2009 ◽  
Vol 36 (9) ◽  
pp. 2018-2023 ◽  
Author(s):  
Laura Niven ◽  
Teresa E. Steele ◽  
Hannes Finke ◽  
Tim Gernat ◽  
Jean-Jacques Hublin
Keyword(s):  
Structured Light ◽  
Structured Light Scanner

Thermodynamics of James Watt – Ignored or not Understood?

2021 ◽  
Vol 42 (3) ◽  
pp. 397
Author(s):  
Jovan Mitrovic
Keyword(s):  
Latent Heat ◽  
Heat Engine ◽  
Primary Source ◽  
Rankine Cycle ◽  
Theoretical Work ◽  
Steam Pressure ◽  
Steam Engine ◽  
Saturated Steam ◽  
Maximum Efficiency ◽  
Starting Point

In the analysis of the development of thermodynamics as a science, the theoretical work of Sadi Carnot, published in 1824, is generally considered to be the starting point. Carnot studied the cycle of an ideal heat engine and formulated the condition for its maximum efficiency. In this article we examine James Watt’s contributions to the formation of fundamental concepts of thermodynamics, made in the course of his work on improving the Newcomen engine and developing his own steam engine. It is shown that Watt was the first to characterize thermodynamic properties such as latent heat and vapor density. The authors prove Watt’s priority in the studies of the dependence of saturated steam pressure on temperature, in which a critical point was found when the latent heat disappears. These results of Watt anticipated by many decades the studies on the thermodynamic critical state by Th. Andrews and J. Thomson. The article also discusses Wattʼs research on thermodynamic cycles. It is shown that he was the first to study the Rankine cycle with superheated steam, known from the history of thermodynamics. Watt was also the first scientist to introduce the concept of a steam engine’ volumetric work as the product of pressure and volume, and developed a device, the steam pressure indicator, to measure its value. We show the results obtained by Watt with steam to be considerably ahead of Prescott Jouleʼs work on the cooling and condensation of gases during expansion. The article presents an interpretation of Watt’s 1769 patent that is very important as the primary source for a subsequent study and establishment of the principles of energy conversion. The factual material presented in this article suggests that Watt’s scientific research have not been properly understood or simply went unnoticed.

Abstract P319: Can Deep Learning Find the Ischemic Core on CT? Transfer Learning From Pre-Trained MRI-Based Networks

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Yannan Yu ◽  
Soren Christensen ◽  
Yuan Xie ◽  
Enhao Gong ◽  
Maarten G Lansberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ground Truth ◽  
Learning Model ◽  
Fine Tuning ◽  
Learning Models ◽  
Starting Point ◽  
Stroke Lesion ◽  
Ischemic Core ◽  
Deep Learning Model

Objective: Ischemic core prediction from CT perfusion (CTP) remains inaccurate compared with gold standard diffusion-weighted imaging (DWI). We evaluated if a deep learning model to predict the DWI lesion from MR perfusion (MRP) could facilitate ischemic core prediction on CTP. Method: Using the multi-center CRISP cohort of acute ischemic stroke patient with CTP before thrombectomy, we included patients with major reperfusion (TICI score≥2b), adequate image quality, and follow-up MRI at 3-7 days. Perfusion parameters including Tmax, mean transient time, cerebral blood flow (CBF), and cerebral blood volume were reconstructed by RAPID software. Core lab experts outlined the stroke lesion on the follow-up MRI. A previously trained MRI model in a separate group of patients was used as a starting point, which used MRP parameters as input and RAPID ischemic core on DWI as ground truth. We fine-tuned this model, using CTP parameters as input, and follow-up MRI as ground truth. Another model was also trained from scratch with only CTP data. 5-fold cross validation was used. Performance of the models was compared with ischemic core (rCBF≤30%) from RAPID software to identify the presence of a large infarct (volume>70 or >100ml). Results: 94 patients in the CRISP trial met the inclusion criteria (mean age 67±15 years, 52% male, median baseline NIHSS 18, median 90-day mRS 2). Without fine-tuning, the MRI model had an agreement of 73% in infarct >70ml, and 69% in >100ml; the MRI model fine-tuned on CT improved the agreement to 77% and 73%; The CT model trained from scratch had agreements of 73% and 71%; All of the deep learning models outperformed the rCBF segmentation from RAPID, which had agreements of 51% and 64%. See Table and figure. Conclusions: It is feasible to apply MRP-based deep learning model to CT. Fine-tuning with CTP data further improves the predictions. All deep learning models predict the stroke lesion after major recanalization better than thresholding approaches based on rCBF.

Shape measurement of specular/diffuse complex surface based on structured light

2020 ◽  
Vol 49 (3) ◽  
pp. 303015-303015
Author(s):  
张宗华 Zonghua Zhang ◽  
刘小红 Xiaohong Liu ◽  
郭志南 Zhinan Guo ◽  
高楠 Nan Gao ◽  
孟召宗 Zhaozong Meng
Keyword(s):  
Structured Light ◽  
Complex Surface ◽  
Shape Measurement

scholarly journals A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 807
Author(s):  
Cong Shi ◽  
Zhuoran Dong ◽  
Shrinivas Pundlik ◽  
Gang Luo
Keyword(s):  
Embedded System ◽  
Optical Flow ◽  
Random Forests ◽  
Visual Motion ◽  
Hardware Acceleration ◽  
Ground Truth ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Time To Collision ◽  
Dense Optical Flow

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out.

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