Introduction to Image Processing

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Exam Paper Format

• Duration: One Hour

• Format: Answer 3 out of 4 Questions

Question 1• Digital Image Fundamentals

– image sensing & acquisition; sampling & quantisation; spatial resolution & aliasing; image representations; colour models: additive vs. subtractive; alternative colour spaces: RGB, HSV, CMYK, YUV

• Point Processing

– intensity transformation: contrast reversal (negatives), log, power-law; contrast stretching using piecewise linear transform functions; bit-plane slicing; histogram equalisation & matching; arithmetic/logic operations

Question 2

• Neighbourhood Processing (Image Filtering)

– noise models: uniform, Gaussian, impulse & periodic; convolution vs. correlation; linear filters: mean (box), weighted average & Gaussian; nonlinear filters: minimum, maximum & median; bilateral filtering; handling of edge pixels; separable filters; edge detectors: 1st and 2nd derivatives gradient operators; Roberts, Prewitt, Sobel, Laplacian operators & their mathematical formulations

Question 3

• Image Segmentation

– purpose and main approaches of segmentation; over & under-segmentation; thresholding: global, local, adaptive and multi-level; algorithms: iterative global thresholding & Otsu, connected components & 2-pass labelling

– texture, region growing, split & merge and motion segmentation; background extraction; edge linking, i.e. local vs. global processing using Hough transform (no mathematical details required)

– 4 vs. 8-neighbours; object representation & description, e.g. normalised chain codes & shape number

Question 4

• Spectral Techniques

– Fourier theory and discrete Fourier transform; magnitude and phase of image spectra; filtering in the frequency domain and its advantages; convolution theorem; basic filtering steps in the frequency domain; differences between low, high & bandpass filters; ideal vs. practical spectral filters, e.g. Butterworth filters; image enhancement vs. image restoration; inverse filters