Infrared imaging and machine vision

Mériaudeau, Fabrice

doi:10.1117/12.713584

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…Many machine vision industrial applications rely on thermal features generated from the surface of objects to monitor quality control (Al-Habaibeh et al, 2004;Maldague, 1994;Meriaudeau, 2007), but they normally involve feature generation in a controlled indoors environment using a active thermal excitation to monitor packaging standards and detect anomalies in products.…”

Section: Figurementioning

confidence: 99%

Passive infrared thermographic imaging for mobile robot object identification

Fehlman

Hinders

2009

Journal of Field Robotics

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The usefulness of thermal infrared imaging as a mobile robot sensing modality is explored, and a set of thermalphysical features used to characterize passive thermal objects in outdoor environments is described. Objects that extend laterally beyond the thermal camera's field of view, such as brick walls, hedges, picket fences, and wood walls, as well as compact objects that are laterally within the thermal camera's field of view, such as metal poles and tree trunks, are considered. Classification of passive thermal objects is a subtle process because they are not a source for their own emission of thermal energy. A detailed analysis is included of the acquisition and preprocessing of thermal images, as well as the generation and selection of thermal-physical features from these objects within thermal images. Classification performance using these features is discussed, as a precursor to the design of a physics-based model to automatically classify these objects. Published

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Section: Figurementioning

confidence: 99%

Passive infrared thermographic imaging for mobile robot object identification

Fehlman

Hinders

2009

Journal of Field Robotics

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“…They are also used for visualization through scattering media like mist, fog, or smoke [ 6 , 7 ]. At present, these techniques are commonly applied in diverse fields such as in biomedicine [ 8 ], surveillance, defense [ 9 ], food control [ 10 ], pests/crops monitoring [ 11 ], cultural heritage [ 12 ], machine vision systems [ 13 ], and more. Furthermore, active imaging in short-wave IR (SWIR) and mid-wave IR (MWIR) wavelengths would benefit from eye-safe (1.55-μm band) and low attenuation windows (1.55-μm, 2-μm, and 3–5-μm bands), being particularly suited for remote sensing applications [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Up-Conversion Sensing of 2D Spatially-Modulated Infrared Information-Carrying Beams with Si-Based Cameras

Torregrosa

Karamehmedović

Maestre

et al. 2020

Sensors

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Up-conversion sensing based on optical heterodyning of an IR (infrared) image with a local oscillator laser wave in a nonlinear optical sum-frequency mixing (SFM) process is a practical solution to circumvent some limitations of IR image sensors in terms of signal-to-noise ratio, speed, resolution, or cooling needs in some demanding applications. In this way, the spectral content of an IR image can become spectrally shifted to the visible/near infrared (VIS/NWIR) and then detected with silicon focal plane arrayed sensors (Si-FPA), such as CCD/CMOS (charge-coupled and complementary metal-oxide-semiconductor devices). This work is an extension of a previous study where we recently introduced this technique in the context of optical communications, in particular in FSOC (free-space optical communications). Herein, we present an image up-conversion system based on a 1064 nm Nd3+: YVO4 solid-state laser with a KTP (potassium titanyl phosphate) nonlinear crystal located intra-cavity where a laser beam at 1550 nm 2D spatially-modulated with a binary Quick Response (QR) code is mixed, giving an up-converted code image at 631 nm that is detected with an Si-based camera. The underlying technology allows for the extension of other IR spectral allocations, construction of compact receivers at low cost, and provides a natural way for increased protection against eavesdropping.

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Thermal Feature Generation

2009

Mobile Robot Navigation With Intelligent Infrared Image Interpretation

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Infrared imaging and machine vision

Cited by 4 publications

References 37 publications

Passive infrared thermographic imaging for mobile robot object identification

Passive infrared thermographic imaging for mobile robot object identification

Up-Conversion Sensing of 2D Spatially-Modulated Infrared Information-Carrying Beams with Si-Based Cameras

Thermal Feature Generation

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