Automatic image exploitation system for small UAVs

Heinze, Norbert; Esswein, Markus; Krüger, Wolfgang; Saur, Günter

doi:10.1117/12.782306

Cited by 21 publications

(12 citation statements)

References 5 publications

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“…UAVs can carry payloads such as cameras, infrared cameras, and other sensors. Thus, they enable us to obtain a synoptic view of an area, which is helpful in applications such as surveillance and reconnaissance, environmental monitoring, disaster assessment and management (see, e.g., [1][2][3]). The aim of this paper is to present a novel method for mosaicking of UAV imagery to obtain visually satisfactory mosaics (not orthomosaics) that are not intended for precise measurement purposes.…”

Section: Introductionmentioning

confidence: 99%

“…In many digital earth applications, it is therefore necessary to stitch hundreds or even thousands of images together to create a larger image that can provide good overall situational awareness. Mosaicking of UAV imagery usually requires extra information, such as camera calibration parameters, position and orientation data from GPS/IMU, ground control points (GCPs) or a reference map, to achieve accurate mosaicking results [1][2][3][4][5][6][7]. When GPS/IMU data are not accurate enough for direct orientation determination, pose estimation using a 3D reconstruction method is usually employed to refine the camera A single image captured from a typical UAV covers only a limited area on the ground.…”

Section: Introductionmentioning

confidence: 99%

“…However, stitching hundreds, or even thousands, of images would result in a significant drift of parameters due to accumulated errors. Therefore, to obtain large mosaics, geo-referenced satellite images of the whole region may be used as a reference image [2,5] and the smaller mosaics are overlaid on top of the reference map using manually determined control-points. A framework where online mosaicking is used to compute and improve the localization of a UAV is presented in [16].…”

Section: Introductionmentioning

confidence: 99%

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Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

et al. 2016

Remote Sensing

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Abstract:The mosaicking of Unmanned Aerial Vehicle (UAV) imagery usually requires information from additional sensors, such as Global Position System (GPS) and Inertial Measurement Unit (IMU), to facilitate direct orientation, or 3D reconstruction approaches (e.g., structure-from-motion) to recover the camera poses. In this paper, we propose a novel mosaicking method for UAV imagery in which neither direct nor indirect orientation procedures are required. Inspired by the embedded deformation model, a widely used non-rigid mesh deformation model, we present a novel objective function for image mosaicking. Firstly, we construct a feature correspondence energy term that minimizes the sum of the squared distances between matched feature pairs to align the images geometrically. Secondly, we model a regularization term that constrains the image transformation parameters directly by keeping all transformations as rigid as possible to avoid global distortion in the final mosaic. Experimental results presented herein demonstrate that the accuracy of our method is twice as high as an existing (purely image-based) approach, with the associated benefits of significantly faster processing times and improved robustness with respect to reference image selection.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

et al. 2016

Remote Sensing

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“…Heinze et.al. [16] e.g. describe an automatic image exploitation system for small UAVs, in which many image enhancement and modeling techniques are presented.…”

Section: Introductionmentioning

confidence: 99%

Image processing in aerial surveillance and reconnaissance: from pixels to understanding

et al. 2013

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Surveillance and reconnaissance tasks are currently often performed using an airborne platform such as a UAV. The airborne platform can carry different sensors. EO/IR cameras can be used to view a certain area from above. To support the task from the sensor analyst, different image processing techniques can be applied on the data, both in real-time or for forensic applications. These algorithms aim at improving the data acquired to be able to detect objects or events and make an interpretation of those detections. There is a wide range of techniques that tackle these challenges and we group them in classes according to the goal they pursue (image enhancement, modeling the world object information, situation assessment). An overview of these different techniques and different concepts of operations for these techniques are presented in this paper.

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“…In recent years, it has shown a wide range of application prospects in occasions that require real-time image mosaic technology such as the field of UAV surveillance and reconnaissance 4 . When digital images are mosaicked, brightness difference between to-be-mosaicked images will result in mosaic artifacts in the final mosaicked image due to non-uniformity caused by optical system vignetting as well as gain changes automatically caused by scene changes.…”

Section: Introductionmentioning

confidence: 99%

An objective assessment method of digital image mosaic artifacts visibility based on visual perception

Jin

Liu³

2010

SPIE Proceedings

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The difference of illumination between to-be-mosaicked images will cause mosaic artifacts when digital images are mosaicked. An objective assessment method of digital images mosaic artifacts visibility based on human visual perception has been studied in this paper. The process of the method are as follows; 1) the gradient errors image is obtained according to the to-be-mosaicked images, 2) the just noticeable difference (JND) of reference image is derived by considering the human visual frequency sensitivity, the brightness mask effects and texture mask effects on visual resolution comprehensively; 3) the mosaic artifacts image which is perceptible visually can be acquired by subtracting the JND threshold values from the wavelet coefficients of gradient errors image in wavelet domain. The mosaic artifacts visibility (MAV) of digital image is constructed to use as an objective assessment index of image stitching seam visibility by considering the average value and information entropy of the mosaic artifacts image comprehensively. The experiment indicates that the objective assessment results of digital image mosaic artifacts visibility by MAV index are consistent with those of the subjective perceptual method basically.

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Automatic image exploitation system for small UAVs

Cited by 21 publications

References 5 publications

Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

Image processing in aerial surveillance and reconnaissance: from pixels to understanding

An objective assessment method of digital image mosaic artifacts visibility based on visual perception

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