scholarly journals Binocular Vision Calibration Method for Long-wavelength Infrared Camera and Visible Spectrum Camera with Different Resolution

2020 ◽  
Author(s):  
li cai ◽  
Wu Qinqin ◽  
Wang Yuanqing
Keyword(s):  
Binocular Vision ◽  
Infrared Camera ◽  
Visible Spectrum ◽  
Calibration Method ◽  
Long Wavelength ◽  
Long Wavelength Infrared

Construction of the Keck long-wavelength infrared camera

1993 ◽  
Author(s):  
John F. Arens ◽  
J. G. Jernigan ◽  
John H. Lacy
Keyword(s):  
Infrared Camera ◽  
Long Wavelength ◽  
Long Wavelength Infrared

Long wavelength infrared 128*128 Al/sub x/Ga/sub 1-x/As/GaAs quantum well infrared camera and imaging system

1993 ◽  
Vol 40 (11) ◽  
pp. 1957-1963 ◽  
Author(s):  
G.C. Bethea ◽  
B.F. Levine ◽  
M.T. Asom ◽  
R.E. Leibenguth ◽  
J.W. Stayt ◽  
...  
Keyword(s):  
Quantum Well ◽  
Imaging System ◽  
Infrared Camera ◽  
Long Wavelength ◽  
Long Wavelength Infrared

scholarly journals Melamine Faced Panels Defect Classification beyond the Visible Spectrum

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3644
Author(s):  
Cristhian Aguilera ◽  
Cristhian Aguilera ◽  
Angel Sappa
Keyword(s):  
Near Infrared ◽  
Visible Spectrum ◽  
Local Binary Patterns ◽  
Classification Performance ◽  
Support Vector ◽  
Feature Descriptor ◽  
Long Wavelength ◽  
Spectral Bands ◽  
Image Set ◽  
Long Wavelength Infrared

In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.

Long-wavelength infrared camera (LWIRC): a 10-μm camera for the Keck telescope

1998 ◽  
Author(s):  
Edward H. Wishnow ◽  
William C. Danchi ◽  
Peter G. Tuthill ◽  
Ronald E. Wurtz ◽  
J. G. Jernigan ◽  
...  
Keyword(s):  
Infrared Camera ◽  
Long Wavelength ◽  
Long Wavelength Infrared

Long-wavelength infrared camera for the Keck Telescope

1990 ◽  
Author(s):  
John F. Arens ◽  
Roger Ball ◽  
J. G. Jernigan ◽  
John H. Lacy ◽  
Robert J. Pernic
Keyword(s):  
Infrared Camera ◽  
Long Wavelength ◽  
Long Wavelength Infrared

Fulfilling the pedestrian protection directive using a long-wavelength infrared camera designed to meet both performance and cost targets

2006 ◽  
Author(s):  
Jan-Erik Källhammer ◽  
Håkan Pettersson ◽  
Dick Eriksson ◽  
Stéphane Junique ◽  
Susan Savage ◽  
...  
Keyword(s):  
Infrared Camera ◽  
Pedestrian Protection ◽  
Long Wavelength ◽  
Long Wavelength Infrared

scholarly journals Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jong-Hwan Kim ◽  
Seongsik Jo ◽  
Brian Y. Lattimer
Keyword(s):  
Infrared Camera ◽  
Texture Features ◽  
Algorithm Optimization ◽  
Local Conditions ◽  
Long Wavelength ◽  
Multiple Feature ◽  
Fire Smoke ◽  
Thermal Infrared Images ◽  
Long Wavelength Infrared

Locating a fire inside of a structure that is not in the direct field of view of the robot has been researched for intelligent firefighting robots. By classifying fire, smoke, and their thermal reflections, firefighting robots can assess local conditions, decide a proper heading, and autonomously navigate toward a fire. Long-wavelength infrared camera images were used to capture the scene due to the camera’s ability to image through zero visibility smoke. This paper analyzes motion and statistical texture features acquired from thermal images to discover the suitable features for accurate classification. Bayesian classifier is implemented to probabilistically classify multiple classes, and a multiobjective genetic algorithm optimization is performed to investigate the appropriate combination of the features that have the lowest errors and the highest performance. The distributions of multiple feature combinations that have 6.70% or less error were analyzed and the best solution for the classification of fire and smoke was identified.

scholarly journals Phase-Based Calibration Method for a Binocular Vision Sensor

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 44354-44362
Author(s):  
Mingwei Shao ◽  
Pan Wang ◽  
Yanjun Wang
Keyword(s):  
Binocular Vision ◽  
Calibration Method ◽  
Vision Sensor

Low complexity visual UAV track navigation using long‐wavelength infrared

2021 ◽  
Author(s):  
Kent Rosser ◽  
Tran Xuan Bach Nguyen ◽  
Philip Moss ◽  
Javaan Chahl
Keyword(s):  
Low Complexity ◽  
Long Wavelength ◽  
Long Wavelength Infrared
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