Read e-book online Analysis of superoscillatory wave functions PDF

By Calder M.S., Kempf A.

Strangely, differentiable features may be able to oscillate arbitrarily quicker than theirhighest Fourier part could recommend. The phenomenon is termed superoscillation.Recently, a pragmatic process for calculating superoscillatory services waspresented and it was once proven that superoscillatory quantum mechanical wave functionsshould show a couple of counter-intuitive actual results. Following up onthis paintings, we right here current extra normal equipment which permit the calculation ofsuperoscillatory wave features with custom-designed actual homes. We giveconcrete examples and we turn out effects in regards to the limits to superoscillatory behavior.We additionally provide an easy and intuitive new reason behind the exponential computationalcost of superoscillations

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HMMs were used to model interactions with such static objects in [17] where object trajectories were used to obtain a 4D features vector accounting for object position and size. This feature vector is then used inside a continuous distribution HMM using multi-variate Gaussian for estimation of emission probabilities for detection of interactions associated to static objects. The approach is further extended by modeling the duration of each state which imposes a practical constrain that objects should take a transition from one state to another after certain interval, Hidden-Semi Markov models (HSMMs) were used to model such duration-related transitions.

Vis. 66(1), 83–101 (2006) 12. : A framework for a video analysis tool for suspicious event detection. Multimedia Tools Appl. 35(1), 109–123 (2007) 13. : Knight: a real time surveillance system for multiple and non-overlapping cameras. Proc. of Int. Conf. Multimedia and Expo. 1, 649–652 (2003) 14. : Real-time wide area multicamera stereo tracking. In: Proc. of Int. Conf. Comp. Vis. , vol. 1, pp. 976–983 (2005) 15. : A survey of vision-based trajectory learning and analysis for surveillance. IEEE Trans.

Analysis of results Figure 7 shows sample event detection results on the sequences S1-T1-C3 and S5-T1-G3 of the PETS 2006 dataset. The images show the detection of the object around which the model is built (the bag) and the subsequent sequence of events, namely a warning (unattended baggage) and an alarm (abandoned baggage). 5%. Both the precision and sensitivity scores for PETS dataset are unitary as the object-centric approach selects events associated with detected objects only and the baggage was detected.

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Analysis of superoscillatory wave functions by Calder M.S., Kempf A.

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