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Lecturer(s)
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Bartoněk Luděk, doc. Ing. Ph.D.
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Course content
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1.Signal sampling and reconstruction. The sampling theorem, the Dirac impulse, derive the spectrum of the sampled signal. 2.Reconstruction of the continuous signal from the succession of samples. 3.Fourier transform, discrete Fourier transform (DFT), Fast Fourier Transform (FFT). 4.Stochastic signal processing, signal measurement in the presence of noise. 5.Digital image processing, image representation and image analysis tasks. 6.Digital image. Features a digital image. 7.Discrete linear integral transforms. 8.Filtration of noise and disturbances of edge detection.
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Learning activities and teaching methods
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Lecture
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Learning outcomes
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Bases numerical processing of signals (one-dimensional - processes in time, two-dimensional-pictures). Analog signals(connected) and discreet in time (signals numerical). Processing of signals in real-time.
Evaluation Evaluate the particular methods and principles, explain the aspects and results concerning the given issue, integrate the knowledge, predict the solutions, evaluate the results and outcomes.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Mark
Knowledge of taught topics.
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Recommended literature
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Graubard, S. R. (1998). The Artificial Intelligence Debate. First MIT Press Cambridge, Massachusetts London, England.
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Hlaváč, V., Sedláček, M. (2005). Zpracování signálů a obrazů. ČVUT.
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Hlaváč, V., Šonka, M. (1992). Počítačové vidění. Grada a.s. Praha.
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Jaroslavskij, L., Bajla, I. (1989). Metódy a systémy číslicového spracovania obrazov. ALFA Bratislava.
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Körner, T. W. (1993). Exercises for analysys. Cambridge university press.
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Schalkoff, R. (1992). Pattern Recognition, statistical, structural and neural approaches. John Wiley & Sons, Inc. New York.
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