Mr. Stefan Brüstle Profile

Stefan Brüstle

at Fraunhober IOSB

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Publications (9)

Proceedings Article | 13 May 2019

Object extraction in the context of an image registration workflow

Stefan Brüstle, Thomas Müller

Proc. SPIE. 10992, Geospatial Informatics IX

KEYWORDS: Roads, Sensors, Image segmentation, Image registration, Image quality, Image sensors, Distance measurement

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Proceedings Article | 27 April 2018

Evaluation of different image processing methods in the context of an image registration workflow

Stefan Brüstle

Proc. SPIE. 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII

KEYWORDS: Image visualization, Visualization, Sensors, Image segmentation, Image processing, Image registration, Georeferencing, Image sensors, Distance measurement, Image filtering

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Proceedings Article | 5 October 2017

Video change detection for fixed wing UAVs

Jan Bartelsen, Thomas Müller, Jochen Ring, Klaus Mück, Stefan Brüstle, Bastian Erdnüß, Bastian Lutz, Theresa Herbst

Proc. SPIE. 10432, Target and Background Signatures III

KEYWORDS: Unmanned aerial vehicles, Sensors, Image processing, Video, Image acquisition, Video surveillance, Video processing, Reconnaissance, Improvised explosive devices, Commercial off the shelf technology

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In this paper we proceed the work of Bartelsen et al.1 We present the draft of a process chain for an image based change detection which is designed for videos acquired by fixed wing unmanned aerial vehicles (UAVs). From our point of view, automatic video change detection for aerial images can be useful to recognize functional activities which are typically caused by the deployment of improvised explosive devices (IEDs), e.g. excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of natural disasters, like flooding, imminent danger can be recognized quickly. Due to the necessary flight range, we concentrate on fixed wing UAVs. Automatic change detection can be reduced to a comparatively simple photogrammetric problem when the perspective change between the "before" and "after" image sets is kept as small as possible. Therefore, the aerial image acquisition demands a mission planning with a clear purpose including flight path and sensor configuration. While the latter can be enabled simply by a fixed and meaningful adjustment of the camera, ensuring a small perspective change for "before" and "after" videos acquired by fixed wing UAVs is a challenging problem. Concerning this matter, we have performed tests with an advanced commercial off the shelf (COTS) system which comprises a differential GPS and autopilot system estimating the repetition accuracy of its trajectory. Although several similar approaches have been presented,23 as far as we are able to judge, the limits for this important issue are not estimated so far. Furthermore, we design a process chain to enable the practical utilization of video change detection. It consists of a front-end of a database to handle large amounts of video data, an image processing and change detection implementation, and the visualization of the results. We apply our process chain on the real video data acquired by the advanced COTS fixed wing UAV and synthetic data. For the image processing and change detection, we use the approach of Muller.4 Although it was developed for unmanned ground vehicles (UGVs), it enables a near real time video change detection for aerial videos. Concluding, we discuss the demands on sensor systems in the matter of change detection.

Proceedings Article | 1 May 2017

Pilot study on real-time motion detection in UAS video data by human observer and image exploitation algorithm

Jutta Hild, Wolfgang Krüger, Stefan Brüstle, Patrick Trantelle, Gabriel Unmüßig, Michael Voit, Norbert Heinze, Elisabeth Peinsipp-Byma, Jürgen Beyerer

Proc. SPIE. 10199, Geospatial Informatics, Fusion, and Motion Video Analytics VII

KEYWORDS: Target detection, Defense and security, Human-machine interfaces, Eye, Safety, Detection and tracking algorithms, Visualization, Calibration, Video, Computing systems, Video surveillance, Motion detection, Motion analysis

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Proceedings Article | 18 October 2016

Evaluation of georeferencing methods with respect to their suitability to address unsimilarity between the image to be referenced and the reference image

Stefan Brüstle, Bastian Erdnüß

Proc. SPIE. 10004, Image and Signal Processing for Remote Sensing XXII

KEYWORDS: Roads, Sensors, Image segmentation, Image processing, Image registration, Orthophoto maps, Image sensors, Digital imaging, Distance measurement, Image filtering

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In recent years, operational costs of unmanned aircraft systems (UAS) have been massively decreasing. New sensors satisfying weight and size restrictions of even small UAS cover many different spectral ranges and spatial resolutions. This results in airborne imagery having become more and more available. Such imagery is used to address many different tasks in various fields of application. For many of those tasks, not only the content of the imagery itself is of interest, but also its spatial location. This requires the imagery to be properly georeferenced. Many UAS have an integrated GPS receiver together with some kind of INS device acquiring the sensor orientation to provide the georeference. However, both GPS and INS data can easily become unavailable for a period of time during a flight, e.g. due to sensor malfunction, transmission problems or jamming. Imagery gathered during such times lacks georeference. Moreover, even in datasets not affected by such problems, GPS and INS inaccuracies together with a potentially poor knowledge of ground elevation can render location information accuracy less than sufficient for a given task. To provide or improve the georeference of an image affected by this, an image to reference registration can be performed if a suitable reference is available, e.g. a georeferenced orthophoto covering the area of the image to be georeferenced. Registration and thus georeferencing is achieved by determining a transformation between the image to be referenced and the reference which maximizes the coincidence of relevant structures present both in the former and the latter. Many methods have been developed to accomplish this task. Regardless of their differences they usually tend to perform the better the more similar an image and a reference are in appearance. This contribution evaluates a selection of such methods all differing in the type of structure they use for the assessment of coincidence with respect to their ability to tolerate unsimilarity in appearance. Similarity in appearance is mainly dependent on the following aspects, namely the similarity of abstraction levels (Is the reference e.g. an orthophoto or a topographical map?), the similarity of sensor types and spectral bands (Is the image e.g. a SAR image and the reference a passively sensed one? Was e.g. a NIR sensor used capturing the image while a VIS sensor was used in the reference?), the similarity of resolutions (Is the ground sampling distance of the reference comparable to the one of the image?), the similarity of capture parameters (Are e.g. the viewing angles comparable in the image and in the reference?) and the similarity concerning the image content (Was there e.g. snow coverage present when the image was captured while this was not the case when the reference was captured?). The evaluation is done by determining the performance of each method with a set of image to be referenced and reference pairs representing various degrees of unsimilarity with respect to each of the above mentioned aspects of similarity.

Proceedings Article | 19 May 2015

Collaborative real-time motion video analysis by human observer and image exploitation algorithms

Jutta Hild, Wolfgang Krüger, Stefan Brüstle, Patrick Trantelle, Gabriel Unmüßig, Norbert Heinze, Elisabeth Peinsipp-Byma, Jürgen Beyerer

Proc. SPIE. 9463, Motion Imagery: Standards, Quality, and Interoperability

KEYWORDS: Eye, Detection and tracking algorithms, Visualization, Video, Computing systems, Motion detection, Optical tracking, Algorithm development, Motion analysis, Tolerancing

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Proceedings Article | 9 June 2014

Identification of spatially corresponding imagery using content-based image retrieval in the context of UAS video exploitation

Stefan Brüstle, Daniel Manger, Klaus Mück, Norbert Heinze

Proc. SPIE. 9076, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications XI

KEYWORDS: Content based image retrieval, Visualization, Sensors, Databases, Video, Video surveillance, Image filtering, Bismuth, Artificial intelligence, Image retrieval

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For many tasks in the fields of reconnaissance and surveillance it is important to know the spatial location represented by the imagery to be exploited. A task involving the assessment of changes, e.g. the appearance or disappearance of an object of interest at a certain location, can typically not be accomplished without spatial location information associated with the imagery. Often, such georeferenced imagery is stored in an archive enabling the user to query for the data with respect to its spatial location. Thus, the user is able to effectively find spatially corresponding imagery to be used for change detection tasks. In the field of exploitation of video taken from unmanned aerial systems (UAS), spatial location data is usually acquired using a GPS receiver, together with an INS device providing the sensor orientation, both integrated in the UAS. If during a flight valid GPS data becomes unavailable for a period of time, e.g. due to sensor malfunction, transmission problems or jamming, the imagery gathered during that time is not applicable for change detection tasks based merely on its georeference. Furthermore, GPS and INS inaccuracy together with a potentially poor knowledge of ground elevation can also render location information inapplicable. On the other hand, change detection tasks can be hard to accomplish even if imagery is well georeferenced as a result of occlusions within the imagery, due to e.g. clouds or fog, or image artefacts, due to e.g. transmission problems. In these cases a merely georeference based approach to find spatially corresponding imagery can also be inapplicable. In this paper, we present a search method based on the content of the images to find imagery spatially corresponding to given imagery independent from georeference quality. Using methods from content-based image retrieval, we build an image database which allows for querying even large imagery archives efficiently. We further evaluate the benefits of this method in the context of a video exploitation workflow on the basis of its integration into our video archive system.

Proceedings Article | 16 May 2013

Gaze interaction in UAS video exploitation

Jutta Hild, Stefan Brüstle, Norbert Heinze, Elisabeth Peinsipp-Byma

Proc. SPIE. 8740, Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness

KEYWORDS: Eye, Visual analytics, Visualization, Sensors, Databases, Video, Image resolution, Image analysis, Video surveillance, Image sensors

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Proceedings Article | 22 June 2012

Archiving image sequences considering associated geographical and nongeographical attributes

Stefan Brüstle, Norbert Heinze

Proc. SPIE. 8359, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XI

KEYWORDS: Unmanned aerial vehicles, Data modeling, Visualization, Sensors, Databases, Video, Image quality, Image sensors, Optical tracking, Data centers

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