Robust stereo infrared camera calibration for 3D depth estimation and measurement

2021 ◽  
Author(s):  
Issac Niwas Swamidoss ◽  
Omar K. Almelhi ◽  
Abdulla Alsaadi Al Mansoori ◽  
Amani Bin Amro ◽  
Abdulrahman Mohamed Al Marzooqi ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Infrared Camera ◽  
Depth Estimation

scholarly journals Extension of the Thermographic Signal Reconstruction Technique for an Automated Segmentation and Depth Estimation of Subsurface Defects

2020 ◽  
Vol 6 (9) ◽  
pp. 96
Author(s):  
Alexander Schager ◽  
Gerald Zauner ◽  
Günther Mayr ◽  
Peter Burgholzer
Keyword(s):  
Signal Reconstruction ◽  
Computational Cost ◽  
Infrared Camera ◽  
Poor Performance ◽  
Depth Estimation ◽  
Reconstruction Technique ◽  
Destructive Testing ◽  
Cost Efficient ◽  
Non Destructive ◽  
Subsurface Defects

With increased use of light-weight materials with low factors of safety, non-destructive testing becomes increasingly important. Thanks to the advancement of infrared camera technology, pulse thermography is a cost efficient way to detect subsurface defects non-destructively. However, currently available evaluation algorithms have either a high computational cost or show poor performance if any geometry other than the most simple kind is surveyed. We present an extension of the thermographic signal reconstruction technique which can automatically segment and image defects from sound areas, while also estimating the defect depth, all with low computational cost. We verified our algorithm using real world measurements and compare our results to standard active thermography algorithms with similar computational complexity. We found that our algorithm can detect defects more accurately, especially when more complex geometries are examined.

Corners positioning for binocular ultra-wide angle long-wave infrared camera calibration

Optik ◽  
2020 ◽  
Vol 206 ◽  
pp. 163441
Author(s):  
Ziang Wang ◽  
Bingqi Liu ◽  
Fuyu Huang ◽  
Yichao Chen ◽  
Shuai Zhang ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Infrared Camera ◽  
Wide Angle ◽  
Long Wave ◽  
Long Wave Infrared

Near-Infrared Camera Calibration for Optical Surgical Navigation

2016 ◽  
Vol 40 (3) ◽  
Author(s):  
Ken Cai ◽  
Rongqian Yang ◽  
Qinyong Lin ◽  
Sujuan Liu ◽  
Huazhou Chen ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Near Infrared ◽  
Surgical Navigation ◽  
Infrared Camera ◽  
Optical Surgical Navigation

Calibration Method for Stereovision Measurement of High-Temperature Components Using Two Infrared Cameras

2013 ◽  
Vol 7 (2) ◽  
pp. 163-170
Author(s):  
Le Song ◽  
◽  
Zi-Hui Zhang
Keyword(s):  
High Temperature ◽  
Camera Calibration ◽  
Infrared Imaging ◽  
Signal To Noise Ratio ◽  
Infrared Camera ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Low Contrast ◽  
Vision Measurement ◽  
High Temperature Components

The infrared vision measurement method has some advantages over visible vision in the measurement of high-temperature components. Infrared imaging is based on different imaging principles, however, making it unreasonable to adopt a conventional visiblelight camera calibration method directly. The present study proposes a dual infrared-camera calibration program in which we use the infrared imaging principle to measure high-temperature components threedimensionally. We use two ceramic balls as calibration targets against the background of an external hightemperature radiation source. Multiple feature points are generated from the precise movement of these targets. In order to improve the accuracy of the calibration method, we took the following approaches: A highly accurate edge-detection algorithm is realized by using a fuzzy neural network that is self-learning, self-adaptive, and utilizes fuzzy processing. We thus achieve a low signal-to-noise ratio and low contrast in infrared images. The distance between these two ceramic targets is used as a calibration reference to further reduce the temperature effect and to improve calibration accuracy and efficiency. The calibration results we got are an average residual error of 6.4134 µm and a variance of 2.9205 µm.

scholarly journals Binocular Infrared Camera Calibration Based on Calibration Checkerboard

2019 ◽  
Vol 1345 ◽  
pp. 032014
Author(s):  
Ziang Wang ◽  
Bingqi Liu ◽  
Fuyu Huang ◽  
Huawei Gao
Keyword(s):  
Camera Calibration ◽  
Infrared Camera
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