scholarly journals Polarization-based shape estimation of transparent objects by using raytracing and PLZT camera

2005 ◽  
Author(s):  
Daisuke Miyazaki ◽  
Noriyuki Takashima ◽  
Akira Yoshida ◽  
Eiki Harashima ◽  
Katsushi Ikeuchi
Keyword(s):  
Shape Estimation ◽  
Transparent Objects

scholarly journals Shape Estimation of Transparent Objects by Using Polarization Analyses

2006 ◽  
Vol 2 ◽  
pp. 407-427 ◽  
Author(s):  
Daisuke Miyazaki ◽  
Katsushi Ikeuchi
Keyword(s):  
Shape Estimation ◽  
Transparent Objects

scholarly journals Scanning from heating: 3D shape estimation of transparent objects from local surface heating

2009 ◽  
Vol 17 (14) ◽  
pp. 11457 ◽  
Author(s):  
Gonen Eren ◽  
Olivier Aubreton ◽  
Fabrice Meriaudeau ◽  
L.A. Sanchez Secades ◽  
David Fofi ◽  
...  
Keyword(s):  
Surface Heating ◽  
Shape Estimation ◽  
Local Surface ◽  
3D Shape ◽  
Transparent Objects

scholarly journals Clear Grasp: 3D Shape Estimation of Transparent Objects for Manipulation

2020 ◽  
Author(s):  
Shreeyak Sajjan ◽  
Matthew Moore ◽  
Mike Pan ◽  
Ganesh Nagaraja ◽  
Johnny Lee ◽  
...  
Keyword(s):  
Shape Estimation ◽  
3D Shape ◽  
Transparent Objects

Sub-triangle opacity masks for faster ray tracing of transparent objects

2020 ◽  
Vol 3 (2) ◽  
pp. 1-12
Author(s):  
Holger Gruen ◽  
Carsten Benthin ◽  
Sven Woop
Keyword(s):  
Ray Tracing ◽  
Integrate Approach ◽  
Proof Of Concept ◽  
Test Operation ◽  
Transparent Objects ◽  
Speed Up

We propose an easy and simple-to-integrate approach to accelerate ray tracing of alpha-tested transparent geometry with a focus on Microsoft® DirectX® or Vulkan® ray tracing extensions. Pre-computed bit masks are used to quickly determine fully transparent and fully opaque regions of triangles thereby skipping the more expensive alpha-test operation. These bit masks allow us to skip up to 86% of all transparency tests, yielding up to 40% speed up in a proof-of-concept DirectX® software only implementation.

scholarly journals Real-time Array Shape Estimation Method of Horizontal Suspended Linear Array Based on Non-acoustic Auxiliary Sensors

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Zonglun Che ◽  
Jun Wang ◽  
Jing Zhu ◽  
Bingbing Zhang ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Linear Array ◽  
Estimation Method ◽  
Shape Estimation ◽  
Array Shape Estimation

Direct measurement and matched-field inversion approaches to array shape estimation

1996 ◽  
Vol 21 (4) ◽  
pp. 393-401 ◽  
Author(s):  
W.S. Hodgkiss ◽  
D.E. Ensberg ◽  
J.J. Murray ◽  
G.L. D'Spain ◽  
N.O. Booth ◽  
...  
Keyword(s):  
Direct Measurement ◽  
Shape Estimation ◽  
Array Shape Estimation ◽  
Field Inversion

scholarly journals Inspection of Transparent Objects with Varying Light Scattering Using a Frangi Filter

2021 ◽  
Vol 7 (2) ◽  
pp. 27
Author(s):  
Dieter P. Gruber ◽  
Matthias Haselmann
Keyword(s):  
Light Scattering ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Production Testing ◽  
Transparent Objects ◽  
Frangi Filter ◽  
Anomaly Score ◽  
Difference Images ◽  
New Machine

This paper proposes a new machine vision method to test the quality of a semi-transparent automotive illuminant component. Difference images of Frangi filtered surface images are used to enhance defect-like image structures. In order to distinguish allowed structures from defective structures, morphological features are extracted and used for a nearest-neighbor-based anomaly score. In this way, it could be demonstrated that a segmentation of occurring defects is possible on transparent illuminant parts. The method turned out to be fast and accurate and is therefore also suited for in-production testing.

scholarly journals Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

2021 ◽  
Vol 13 (3) ◽  
pp. 455
Author(s):  
Md Nazrul Islam ◽  
Murat Tahtali ◽  
Mark Pickering
Keyword(s):  
Light Field ◽  
Mean Squared Error ◽  
Specular Reflection ◽  
Similarity Index ◽  
Detection Algorithm ◽  
Color Channel ◽  
Absolute Deviation ◽  
Significant Information ◽  
Transparent Objects ◽  
Transparent Object

Multispectral polarimetric light field imagery (MSPLFI) contains significant information about a transparent object’s distribution over spectra, the inherent properties of its surface and its directional movement, as well as intensity, which all together can distinguish its specular reflection. Due to multispectral polarimetric signatures being limited to an object’s properties, specular pixel detection of a transparent object is a difficult task because the object lacks its own texture. In this work, we propose a two-fold approach for determining the specular reflection detection (SRD) and the specular reflection inpainting (SRI) in a transparent object. Firstly, we capture and decode 18 different transparent objects with specularity signatures obtained using a light field (LF) camera. In addition to our image acquisition system, we place different multispectral filters from visible bands and polarimetric filters at different orientations to capture images from multisensory cues containing MSPLFI features. Then, we propose a change detection algorithm for detecting specular reflected pixels from different spectra. A Mahalanobis distance is calculated based on the mean and the covariance of both polarized and unpolarized images of an object in this connection. Secondly, an inpainting algorithm that captures pixel movements among sub-aperture images of the LF is proposed. In this regard, a distance matrix for all the four connected neighboring pixels is computed from the common pixel intensities of each color channel of both the polarized and the unpolarized images. The most correlated pixel pattern is selected for the task of inpainting for each sub-aperture image. This process is repeated for all the sub-aperture images to calculate the final SRI task. The experimental results demonstrate that the proposed two-fold approach significantly improves the accuracy of detection and the quality of inpainting. Furthermore, the proposed approach also improves the SRD metrics (with mean F1-score, G-mean, and accuracy as 0.643, 0.656, and 0.981, respectively) and SRI metrics (with mean structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (IMMSE), and mean absolute deviation (MAD) as 0.966, 0.735, 0.073, and 0.226, respectively) for all the sub-apertures of the 18 transparent objects in MSPLFI dataset as compared with those obtained from the methods in the literature considered in this paper. Future work will exploit the integration of machine learning for better SRD accuracy and SRI quality.

Sign in / Sign up

Export Citation Format

Share Document