[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

90 Z. Kuś and A. Nawrat
[3] Irani, M., Peleg, S.: Improving resolution by image registration. CVGIP: Graph.
Modelsand Image Process. 53, 231–239 (1991)
[4] Chesnaud, C., Refegier, P., Boulet, V.: Statistical region snake-based segmentation
adapted to different physical noise models. IEEE Trans. Patt. Anal. Mach. Intell. 21,
1145–1157 (1999)
[5] Gordon, N., Ristic, B., Arulampalam, S.: Beyond the Kalman Filter: Particle Filters
for Tracking Applications. Artech House, Boston (2004)
[6] Sharp, C., Shakernia, O., Sastry, S.: A Vision System for Landing an Unmanned
Aerial Vehicle. In: Proceedings of the 2001 IEEE International Conference on Robotics
and Automation, vol. 2, pp. 1720–1727. IEEE, Los Alamitos (2001)
[7] Casbeer, D., Li, S., Beard, R., Mehra, R., McLain, T.: Forest Fire Monitoring With
Multiple Small UAVs, Porland, OR (April 2005)
[8] Kuś, Z., Fraś, S.: Helicopter control algorithms from the set orientation to the set
geographical location. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced
Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp.
3–14. Springer, Heidelberg (2013)
[9] Nawrat, A.: Modelowanie i sterowanie bezzałogowych obiektów latających. Wydawnictwo
Politechniki Slaskiej, Gliwice (2009)
[10] Valavanis, K.P. (ed.): Advances In Unmanned Aerial Vehicles. Springer (2007)
[11] Castillo, P., Lozano, R., Dzul, A.E.: Modelling and Control of Mini-Flying Machines.
Springer (2005)
[12] Padfield, G.D.: Helicopter Flight Dynamics. Backwell Science Ltd. (1996)
[13] Manerowski, J.: Identyfikacja modeli dynamiki ruchu sterowanych obiektów lataja˛cych,
WN ASKON, Warszawa (1999)
[14] Kearney, J.K., Thompson, W.B.: Optical flow estimation: An error analysis of gradient-based
methods with local optimization. IEEE Transactions on Pattern Analysis
and Machine Intelligence 9, 229–243 (1987)
[15] Anandan, P.: A computational framework and an algorithm for the measurement of
visual motion. International Journal of Computer Vision 2, 283–310 (1989)
[16] Barman, H., Haglund, L., Knutsson, H., Granlund, G.: Estimation of velocity, acceleration,
and disparity in time sequences. In: Proc. IEEE Workshop on Visual Motion,
Princeton, NJ, pp. 44–51 (1991)
[17] Bascle, B., Bouthemy, E., Deriche, N., Meyer, E.: Tracking complex primitives in
an image sequence. In: Proc. 12th International Conference on Pattern Recognition,
Jerusalem, pp. 426–431 (1994)
[18] Butt, E.J., Yen, C., Xu, X.: Local correlation measures for motion analysis: A comparativestudy.
In: Pattern Recognition and Image Processing Conference, Las Vegas,
pp. 269–274 (1982)
[19] Butt, E.J., Bergen, J.R., Hingorani, R., Kolczynski, R., Lee, W.A., Leung, A., Lubin,
J., Shvayster, H.: Object tracking with a moving camera. In: Proc. IEEE Workshop
on Visual Motion, Irving, pp. 2–12 (1989)
[20] Buxton, B.E., Buxton, H.: Computation of optical flow from the motion of edges
features in image sequences. Image and Vision Computing 2(2), 59–75 (1984)
[21] Campani, M., Verri, A.: Computing optical flow from an overconstrained system oflinear
algebraic equations. In: Proc. 3rd International Conference on Computer Vision,
Osaka, pp. 22–26 (1990)
[22] Campani, M., Verri, A.: Motion analysis from firstorder properties of optical flow.
CVGIP: Image Understanding 56(1), 90–107 (1992)
[23] Carlsson, S.: Information in the geometric structure of retinal flow field. In: Proc.
2nd International Conference on Computer Vision, pp. 629–633 (1988)
[24] Gessing, R.: Control Fundamentals. Silesian University of Technology, Gliwice
(2004)