Ein Bildsegmentierer für die echtzeitnahe Verarbeitung

Anderer, C.; Thönnessen, U.; Carlsohn, Matthias F.; Klonz, A.

doi:10.1007/978-3-642-75102-8_55

Cited by 8 publications

(9 citation statements)

References 2 publications

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“…The result of detecting MSERs in the reference image and considering intersection yields 42 true alarms and 16 false alarms or in values 0.74 and 0.75 for completeness and correctness, respectively. Finally, using the enhanced version of [18] for detection of hot spots instead of MSERs deteriorates the results: The number of false alarms grows (correctness 0.60) while the completeness measure remains at 0.61.…”

Section: Test Data and Computational Resultsmentioning

confidence: 99%

“…Though any hot spot detector can be used for detecting small elevated regions in the difference of Z loc and the DTM, we decided to use the MSER (Maximally Stable Extremal Regions) method [17]. Its performance has shown advantages in comparison to a similar procedure, namely, an enhanced version of [18]. Also, a fast implementation of this algorithm [19] is available.…”

Section: Hypothesis Generationmentioning

confidence: 99%

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Detection of Small Roof Details in Image Sequences

Bulatov

Pohl

2013

Image Analysis

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Detecting smaller elevated objects, like chimneys, in high resolution images has several important applications, such as collision warning. On the other hand, the already existing 3D models (that already include the terrain, buildings and vegetation) can be enriched by new instances. There are not many contributions about extracting fine roof details in the literature. Therefore, we developed a new, modularized algorithm for detecting these details as hot spots in the local elevation maps; such a map is typically obtained by a multi-view dense matching method. We use explicit and implicit assumptions on data in order to tighten the search range for chimneys and reduce the number of false alarms. Finally, filtering hot spots by means of color or intensity images takes place. Thus, good detection rates can be achieved for a data set consisting of several high resolution images taken over a residential area.

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Section: Test Data and Computational Resultsmentioning

confidence: 99%

Section: Hypothesis Generationmentioning

confidence: 99%

Detection of Small Roof Details in Image Sequences

Bulatov

Pohl

2013

Image Analysis

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“…ISOL is an abbreviation for "Image Segmentation by Optimization of Threshold Levels" [1]. This method has been developed and implemented at FGAN-FOM and was adapted for the use with SLDV data.…”

Section: Analysis Of the Magnitude Informationmentioning

confidence: 99%

SLDV III: the next generation of acousto-optical landmine detection

Klein

Hebel²,

Resch³

2005

SPIE Proceedings

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A new generation of Scanning Laser Doppler Vibrometer (SLDV) has been realized; based on experience and results of a former proof-of-concept design and a number of field tests. This new device SLDV III comprises a number of technical improvements in the transmitter and receiver section as well as in the evaluation of the recorded vibration signals. The subsequent paper summarizes the main features of this instrument.

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“…ISOL is an abbreviation for "Image Segmentation by Optimization of Threshold Levels" [7]. This method was developed and implemented at FGAN-FOM and has been adapted for our task.…”

Section: Extraction Of Point Features By Image Sequence Analysismentioning

confidence: 99%

<title>Automatic 3D object pose estimation in IR image sequences for forward motion applications</title>

Jäger¹,

Hebel²,

Bers³

2004

SPIE Proceedings

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The successful mission of autonomous airborne systems like unmanned aerial vehicles (UAV) strongly depends on the performance of automatic image processing used for navigation, target acquisition and terminal homing. In this paper we propose a method for automatic determination of missile position and orientation (pose) during target approach. The flight path is characterized by a forward motion, i.e. an approximately linear motion along the optical axis of the sensor system. Due to the lack of disparity, classical methods based on stereo triangulation are not suitable for 3D object recognition. To handle this we applied the SoftPOSIT algorithm, originally proposed by D. DeMenthon, and adapted it to fit our specific needs: The gathering of image points is done by multi-threshold segmentation, texture analysis, 2D tracking and edge detection. The reference points are updated in each loop and the calculated pose is smoothed using the quaternion representation of the model's orientation in order to stabilize the computations. We will show some results of image based determination of trajectories for airborne systems. Terminal homing is demonstrated by tracking the 3D pose of a vehicle in an image sequence taken in oblique view and gathered by an infrared sensor mounted to a helicopter.

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Ein Bildsegmentierer für die echtzeitnahe Verarbeitung

Cited by 8 publications

References 2 publications

Detection of Small Roof Details in Image Sequences

Detection of Small Roof Details in Image Sequences

SLDV III: the next generation of acousto-optical landmine detection

<title>Automatic 3D object pose estimation in IR image sequences for forward motion applications</title>

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