[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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Thermal Camera for Autonomous Mobile Platforms 111 Fig. 19. Test stand for determining NUC correction coefficients and bad pixel identification in microbolometer focal plane array Fig. 20. Test stands for the determination of parameters of thermal cameras ( Accredited Laboratory of Institute of Optoelectronics, MUT) Similarly the identification of bad pixels on the basis of detector offset is carried out. Pixel is considered bad if its offset correction coefficient in NUC table is smaller or greater than defined percentage value (e.g. 30%). Using detector noise criteria the pixel is marked as bad if its noise level (RMS) exceeds the defined threshold value. For example, for a given noise level (mean value of 5.0 and standard deviation of 1.0) and a threshold set at 3.5, the pixel is considered bad when its RMS noise value exceeds 8.5. The measurements of basic parameters of the presented thermal camera were performed at the Institute of Optoelectronics on the specialized test stand for the evaluation of thermal cameras [9]. The test stand consists of a collimator, IR radiation standards with control units, rotating test and filter wheels and a computer with video grabber card and specialized software (Fig. 20). The following camera characteristics were measured on this test stand: • modulation transfer function MTF, • signal transfer SiTF, • minimum resolvable temperature difference (MRTD) characteristics, • 3D noise model.
112 G. Bieszczad et al. MTF function describes the image distortions introduced by a camera. It is defined an a module from normalized to unity FFT transform of luminance distribution in a point source image, for null spatial frequency. On the basis of derived MRTD characteristics the detection, recognition and identification ranges were calculated for a standard NATO target, according to STANAG 4347. Spatial noise of an imaging system can be calculated using 3D noise model. Each noise component of a system influences the image quality. The analysis of the contribution of each particular noise source is difficult, or rather impossible, as the influence of different noise sources may result in the same type of image degradation. Thus in order to perform noise analysis the 3D noise model was introduced. In order to determine 3D noise components several images of uniform background were acquired and on this basis the matrix noise model of camera system was created. 6 Summary The presented high resolution thermal camera with microbolometer focal plane array made of amorphous silicon operates in 8-12 μm spectral range. The applied FPA array, made by ULIS (France), has a size of 640x480 pixels, which amounts to the total number of 307 200 infrared detectors. Thermal resolution is 0.08 degrees Celsius and spatial resolution reaches 1.35 mrad. Such parameters allow for the detection of a human being from over 1300 meters’ distance. The camera is equipped with atheromalized lens with single FOV of about 50° and F-number of 1.25. The applied passively athermalized lens can operate in a broad temperature range without the need of man al refocusing. Fig. 21. Sample image produced by the presented high resolution thermal camera
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Studies in Computational Intelligen
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Aleksander Nawrat · Zygmunt Kuś E
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“In God We Trust, All others we o
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VIII Preface valuable information i
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Contents Part I: Design of Object D
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Contents XIII Technology Developmen
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XVI List of Contributors Adam Czorn
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XVIII List of Contributors Henryk M
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XX List of Contributors Józef Wron
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2 Design of Object Detection, Recog
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4 A. Babiarz et al. 1 Introduction
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6 A. Babiarz et al. The description
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12 A. Babiarz et al. a) b) Fig. 7.
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14 A. Babiarz et al. • The camera
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Recognition and Location of Objects
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Object Tracking for Rapid Camera Mo
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Probabilistic Approach to Planning
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206 Practical Applications of Class
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Prototyping the Autonomous Flight A
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Feature Extraction and HMM-Based Cl
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Technology Development of Military
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344 Conclusions Control algorithms
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[Studies in Computational Intelligence 481] Artur Babiarz, Robert Bieda, Karol Jędrasiak, Aleksander Nawrat (auth.), Aleksander Nawrat, Zygmunt Kuś (eds.) - Vision Based Systemsfor UAV Applications (2013, Sprin

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