Indoor and Outdoor Depth Imaging of Leaves With Time-of-Flight and Stereo Vision Sensors: Analysis and Comparison

Wajahat Kazmi, Sergi Foix, Guilliem Alenya, Hans Jørgen Andersen

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

63 Citationer (Scopus)

Resumé

In this article we analyze the response of Time-of-Flight (ToF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo correspondence algorithms (local correlation and graph cuts). PMD CamCube has better cancelation of sunlight, followed by CamBoard, while SwissRanger SR4000 performs poorly under sunlight. Stereo vision is comparatively more robust to ambient illumination and provides high resolution depth data but is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of ToF cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of camera flags (PMD) or confidence matrix (SwissRanger).
OriginalsprogEngelsk
TidsskriftISPRS Journal of Photogrammetry and Remote Sensing
Vol/bind88
Sider (fra-til)128-146
Antal sider19
ISSN0924-2716
DOI
StatusUdgivet - feb. 2014

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Stereo vision
Polarization mode dispersion
leaves
sunlight
Cameras
cameras
sensor
Imaging techniques
sensors
Sensors
Lighting
illumination
texture
Computational efficiency
matrix
cancellation
rooms
dynamic range
confidence
textures

Citer dette

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title = "Indoor and Outdoor Depth Imaging of Leaves With Time-of-Flight and Stereo Vision Sensors: Analysis and Comparison",
abstract = "In this article we analyze the response of Time-of-Flight (ToF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo correspondence algorithms (local correlation and graph cuts). PMD CamCube has better cancelation of sunlight, followed by CamBoard, while SwissRanger SR4000 performs poorly under sunlight. Stereo vision is comparatively more robust to ambient illumination and provides high resolution depth data but is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of ToF cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of camera flags (PMD) or confidence matrix (SwissRanger).",
keywords = "leaf imaging, depth, exposure, time of ight, stereo vision, sunlight",
author = "Wajahat Kazmi and Sergi Foix and Guilliem Alenya and Andersen, {Hans J{\o}rgen}",
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Indoor and Outdoor Depth Imaging of Leaves With Time-of-Flight and Stereo Vision Sensors : Analysis and Comparison. / Kazmi, Wajahat; Foix, Sergi; Alenya, Guilliem; Andersen, Hans Jørgen.

I: ISPRS Journal of Photogrammetry and Remote Sensing, Bind 88, 02.2014, s. 128-146.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Indoor and Outdoor Depth Imaging of Leaves With Time-of-Flight and Stereo Vision Sensors

T2 - Analysis and Comparison

AU - Kazmi, Wajahat

AU - Foix, Sergi

AU - Alenya, Guilliem

AU - Andersen, Hans Jørgen

PY - 2014/2

Y1 - 2014/2

N2 - In this article we analyze the response of Time-of-Flight (ToF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo correspondence algorithms (local correlation and graph cuts). PMD CamCube has better cancelation of sunlight, followed by CamBoard, while SwissRanger SR4000 performs poorly under sunlight. Stereo vision is comparatively more robust to ambient illumination and provides high resolution depth data but is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of ToF cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of camera flags (PMD) or confidence matrix (SwissRanger).

AB - In this article we analyze the response of Time-of-Flight (ToF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo correspondence algorithms (local correlation and graph cuts). PMD CamCube has better cancelation of sunlight, followed by CamBoard, while SwissRanger SR4000 performs poorly under sunlight. Stereo vision is comparatively more robust to ambient illumination and provides high resolution depth data but is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of ToF cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of camera flags (PMD) or confidence matrix (SwissRanger).

KW - leaf imaging, depth, exposure, time of ight, stereo vision, sunlight

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DO - 10.1016/j.isprsjprs.2013.11.012

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JO - ISPRS Journal of Photogrammetry and Remote Sensing

JF - ISPRS Journal of Photogrammetry and Remote Sensing

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