scholarly journals Deflectometric method for surface shape reconstruction from its gradient

2012 ◽  
Vol 20 (27) ◽  
pp. 28341 ◽  
Author(s):  
Antonin Miks ◽  
Jiri Novak
Keyword(s):  
Shape Reconstruction ◽  
Surface Shape

scholarly journals Large-scale surface shape sensing with  learning-based computational mechanics            

2021 ◽  
Author(s):  
Kui Wang ◽  
Chi Hin Mak ◽  
Justin Di-Lang Ho ◽  
Zhi-Yu Liu ◽  
Kam Yim Sze ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Mechanics ◽  
Hybrid Approach ◽  
Shape Reconstruction ◽  
Surface Shape ◽  
Morphological Data ◽  
Design Parameters ◽  
Fe Model ◽  
Flexible Sensor ◽  
Continuous Surface

Proprioception, the ability to perceive one’s own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, we propose a flexible sensor framework that incorporates a novel hybrid modeling strategy, taking advantage of computational mechanics and machine learning. We implement the sensor framework on a large, thin and flexible sensor that transforms sparsely distributed strains into continuous surface shape. Finite element (FE) analysis is utilized to determine sensor design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real-time, robust and high-order surface shape reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated and reported on such a large-scale (A4-paper-size) sensor before.

scholarly journals Supporting Information for "Large-scale Surface Shape Sensing with  Learning-based Computational Mechanics"             

2021 ◽  
Author(s):  
Kui Wang ◽  
Chi Hin Mak ◽  
Justin Di-Lang Ho ◽  
Zhi-Yu Liu ◽  
Kam Yim Sze ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Mechanics ◽  
Hybrid Approach ◽  
Shape Reconstruction ◽  
Surface Shape ◽  
Morphological Data ◽  
Design Parameters ◽  
Fe Model ◽  
Flexible Sensor ◽  
Continuous Surface

Proprioception, the ability to perceive one’s own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, we propose a flexible sensor framework that incorporates a novel hybrid modeling strategy, taking advantage of computational mechanics and machine learning. We implement the sensor framework on a large, thin and flexible sensor that transforms sparsely distributed strains into continuous surface shape. Finite element (FE) analysis is utilized to determine sensor design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real-time, robust and high-order surface shape reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated and reported on such a large-scale (A4-paper-size) sensor before.

Absolute optical flatness testing by surface shape reconstruction using Zernike polynomials

2018 ◽  
Vol 57 (09) ◽  
pp. 1 ◽  
Author(s):  
Haoyu Lyu ◽  
Yuanshen Huang ◽  
Bin Sheng ◽  
Zhengji Ni
Keyword(s):  
Shape Reconstruction ◽  
Surface Shape ◽  
Zernike Polynomials

scholarly journals Crosscorrelation image processing for surface shape reconstruction using fiducial markers

2021 ◽  
Vol 2127 (1) ◽  
pp. 012030
Author(s):  
E V Shmatko ◽  
V V Pinchukov ◽  
A D Bogachev ◽  
A Yu Poroykov
Keyword(s):  
Correlation Function ◽  
Cross Correlation ◽  
Three Dimensional ◽  
Shape Reconstruction ◽  
Surface Shape ◽  
Optical Methods ◽  
Shape Measurement ◽  
Fiducial Markers ◽  
Correlation Processing ◽  
Function Calculation

Abstract Optical methods for deformations diagnostic and surface shape measurement are widely used in scientific research and industry. Most of these methods are based on triangulating a set of two-dimensional points in the images appropriate to the same three-dimensional points of the object in space. Various algorithms to search such points are applied. The possibility of using cross-correlation processing of digital images to search these points is considered in the work. Algorithms based on the correlation function calculation are widely employed in such a popular flow diagnostic method as PIV. The cameras of a stereo system for surface shape measurement can be widely spaced, and the tilt angles relative to the surface can differ significantly. This leads to the fact that the images taken from the cameras cannot be directly processed by the correlation function because it is not invariant to rotation. To solve this problem, fiducial markers are used to find an initial estimate of displacement of the images relative to each other. This approach makes it possible to successfully apply correlation processing for stereo system images with a large stereo base.

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