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Image and signal processing

Examiners: Cecília Sikné Lányi (DSc), Zoltán Kató (DSc), Gyula Simon (DSc), coordinator: László Czúni (PhD)

Exam material: 6 freely chosen topics from the subject matter, which the examinee agrees with the examiner in advance

Thematics:

1. The structure and operation of the human visual system. The limits of the perception of image information. Visual attention and comfort. Shape recognition, reading.

2. Basic concepts of color theory. Color vision and color vision defects. Basic theories of radiometry and photometry. Color display models, color systems. Color difference calculation.

3. Interaction of displayed image information with the human visual system. Color reproduction on different media. Color-correct design. Color harmonies. Color management.

4. Image measurements and image pattern recognition. Feature extraction (SIFT, SURF, FAST). Haar-like feature detector, and the cascade algorithm. Basic algorithms of classification and clustering. Bag-of-Words methods.

5. Image filters and Markov models in image processing. Image noise models. Linear and non-linear filters (convolutions, median filter, Wiener filter, Wallis filter). Image morphology. 2D Markov Random field models. Hidden Markov models in image recognition.

6. Image transformations and their applications: color transformations, geometric transformations, coordinate transformations, camera calibration. Unitary transformations (Fourier, Cosine, Hadamard, Karhunen Loeve), filters in the frequency domain, transform image compression, feature extraction.

7. Basics of digital signal processing. Discrete-time signals and systems, their representations in the time and in the frequency domain. Fourier transform, DFT, FFT, z-transform.

8. Sampling of continuous signals. Sampling in time domain, sampling theory, applications of under- and over-sampling. Sampling in the amplitude range: quantization, error characterization. A/D and D/A converters.

9. The discrete Fourier transform (DFT) and its applications. Properties of DFT. Windowing and its application. The Fast Fourier Transform. Circular and linear convolution. The spectrogram and periodogram.

10. Adaptive filters and their applications. Optimal linear filtering: the Wiener filter. Derivation of the Wiener filter. The Wiener-Hopf equation. Derivation of the LMS algorithm. Variants of the algorithm. Typical applications of the LMS algorithm. Derivation of the Kálmán filter. Applications of the Kálmán filter.

11. Tools and methods of medical measurements. Measurement of basic biophysical quantities, sensors, sensor properties. Measurement techniques and signal processing procedures of medical imaging procedures. Image reconstruction procedures, approximation procedures. Factors determining the resolution of signal and image processing procedures.

12. Signal and image processing procedures of medical measurement technology. Basic properties of biophysical signals, variability of biosignals. Noise filters, improvement of the signal/noise ratio, segmentation of signals and images, waveform recognition, edge enhancement. Parameters carrying diagnostic information, transformation methods for information extraction. Information carriers of medical images, classification problems and methods.