[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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44 R. Bieda et al. where and are respectively the covariance matrix and the mean class vector , 1,…,4, designated for vectors forming a class after the FLD transformation. The process of the classification with the use of function (44) processes as follows : Feature vector belongs to class when: max , ,…, , (43) In case, where the zoom ratio will be set on a bit lover level ( for e.g. 96%) then the dimension of feature vectors after FLD transformation will reduce itself to the one-dimension space ( 1). The arrangement of the classes can be described as an outcome of projection of the plane on picture 6b on axis. In this case, the classification process can be carried out on the basis of the minimizing the differences between the feature and the mean value of this feature for the particular classes: min | | (44) ,.. The outcome of the activity of the classifier which bases on the above example is show on Fig 7. a. b. Fig. 7. The outcome of the classification in the dimension of the features after the FLD transformation : a. case 2D b. case 1D As it can be noticed in Fig 7 in both cases the outcome of the classification is correct. This gives hope for the construction of a autonomic visual system. Such system, ‘pre-learned” is able to independently analyze the image of the surroundings of the manipulator and send him the data needed to completion of the overriding task. 8 Summary The results obtained show that the construction of the co-operating classifier and the visual system is possible. The obtained results are promising and appear to
Recognition and Location of Objects in the Visual Field of a UAV Vision System 45 augur well for the further work on this issue. Additionally, in the above, shortened description of the issue, it was shown which problems must be solved to construct such a classifier. References [1] Obinata, G., Dutta, A.: Vision-Systems Segmentation and Pattern Recognition. I_Tech Education and Publishing (2007) [2] Russ, J.C.: The Image Processing Handbook, 5th edn. CRC Pressm, Taylor and Francis Group (2007) [3] Webb, A.R.: Statistical Pattern Recognition, 2nd edn. Wiley (2002) [4] Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press (2009) [5] Gonzalez, R.C., Wood, R.E.: Digital Image Processing, 3rd edn. Prentice Hall (2008) [6] Tadeusiewicz, R., Korohoda, P.: Komputerowa analiza i przetwarzanie obrazów. Fundacja Postępu Telekomunikacji (1997) [7] Nixon, M.S., Aguado, A.S.: Feature Extraction & Image Processing, 2nd edn. Academic Press (2008) [8] Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006) [9] Nawrat, A., Daniec, K., Warmuz, T.: Object detection using IR camera. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems. SCI, vol. 440, pp. 129–142. Springer, Heidelberg (2013) [10] Daniec, K., Jedrasiak, K., Koteras, R., Nawrat, A.: Embedded micro inertial navigation system. Applied Mechanics and Materials 249-250, 1234–1246 (2013) [11] Jędrasiak, K., Nawrat, A., Wydmańska, K.: SETh-link the distributed management system for unmanned mobile vehicles. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems. SCI, vol. 440, pp. 247–256. Springer, Heidelberg (2013) [12] Iwaneczko, P., Jędrasiak, K., Daniec, K., Nawrat, A.: A prototype of unmanned aerial vehicle for image acquisition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 87–94. Springer, Heidelberg (2012) [13] Jȩdrasiak, K., Nawrat, A.: Image recognition technique for unmanned aerial vehicles. In: Bolc, L., Kulikowski, J.L., Wojciechowski, K. (eds.) ICCVG 2008. LNCS, vol. 5337, pp. 391–399. Springer, Heidelberg (2009)
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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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Omnidirectional Video Acquisition D
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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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