[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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270 H. Josiński et al. [3] Liu, H., Setiono, R.: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, pp. 319–327 (1996) [4] Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997) [5] Boyd, J.E., Little, J.J.: Biometric Gait Recognition. In: Tistarelli, M., Bigun, J., Grosso, E. (eds.) Biometrics School 2003. LNCS, vol. 3161, pp. 19–42. Springer, Heidelberg (2005) [6] Michalczuk, A., Świtoński, A., Josiński, H., Polański, A., Wojciechowski, K.: Gait identification based on MPCA reduction of a video recordings data. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 525–532. Springer, Heidelberg (2012) [7] Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous valued attributes for classification learning. In: Thirteenth International Joint Conference on Artificial Intelligence, pp. 1022–1027 [8] Tacoob, Y.: Parameterized Modeling and Recognition of Activities. Computer Vision and Image Understanding 73(2), 232–247 [9] Kulbacki, M., Segen, J., Bak, A.: Unsupervised learning motion models using dynamic time warping. In: Proc. Symp. Intell. Inf. Syst., pp. 217–226 [10] Iwamoto, K., Sonobe, K., Komatsu, N.: A Gait Recognition Method using HMM. In: Proceedings of the 9th European Conference on Computer Vision, pp. 1936– 1941. IEEE [11] Liang, W., Tieniu, T., Huazhong, N., Weiming, H.: Silhouette Analysis-Based Gait Recognition for Human Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12) [12] Sminchisescu, C., Jepson, A.: Generative Modeling for Continuous Non-Linearly EmbeddedVisual Inference. In: ICML [13] Witten, I.H., Frank, E.: Data Mining: Practical Machnine Learning Tool and Techniques [14] Vasilescu, M., Terzopoulos, D.: Multilinear Independent Components Analysis. In: Proceedings of the IEEE Computer Vision and Pattern Recognition Conference [15] Cheng, M.-H., Hoa, M.-F., Huanga, C.-L.: Gait analysis for human identification through manifold learning and HMM. Pattern Recognition 41(8), 2541–2553 [16] Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: MPCA: Multilinear Principal Component Analysis of Tensor Objects. IEEE Transactions on Neural Networks 19(1), 18–39 [17] Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization. IEEE Transactions on Neural Networks 20(11), 1820–1836 [18] Pushpa, R.M., Arumugam, G.: An efficient gait recognition system for human identification using modified ICA. International Journal of Computer Science & Information Technology 2(1) [19] Benbakreti, S., Benyettou, M.: Recognition Human by gait using PCA and DTW. In: 3th International Conference on Computer Science and its Applications [20] Josinski, H., Switonski, A., Michalczuk, A., Wojciechowski, K.: Motion Capture as data source for gait-based human identification. Przegląd Elektrotechniczny 88(12B), 201–204 (2012)
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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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10 A. Babiarz et al. o o o “micvo
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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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94 Construction of Image Acquisitio
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96 G. Bieszczad et al. effectivenes
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100 G. Bieszczad et al. Table 1. No
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112 G. Bieszczad et al. MTF functio
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114 G. Bieszczad et al. [11] Krupi
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116 D. Bereska et al. Presented in
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118 D. Bereska et al. Fig. 2. Image
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120 D. Bereska et al. Fig. 4. Ortho
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Omnidirectional Video Acquisition D
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Part III Design of Vision Based Con
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Vision System for Group of Mobile R
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A Distributed Control Group of Mobi
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178 D. Bereska et al. The aim of th
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184 D. Bereska et al. 4.1 Mechanica
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186 D. Bereska et al. 4.1.3 Open-Lo
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188 D. Bereska et al. Fig. 16. Comp
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Probabilistic Approach to Planning
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206 Practical Applications of Class
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208 M. Mellado and K. Skrzypczyk an
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328 M. Koźlak, A. Kurzeja, and A.
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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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