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Detection of Regions of Interest

This chapter covers thresholding and binarization, detection of isolated points and lines, edge detection using convolution mask operators, Laplacian of the Gaussian, scale-space methods, Canny's method, Fourier-domain methods, and edge linking.

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Detection of Regions of Interest

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  1. Detection of Regions of Interest Chapter 5 Yevhen Hlushchuk, BRU, LTL

  2. Tentative plan • Thresholding and binarization • Detection of isolated points and lines • Edge detection • convolution mask operators • the Laplacian of the Gaussian • scale-space methods • Canny’s method • fourier-domain methods • edge linking

  3. Thresholding and binarization

  4. Detection of isolated points and lines • The main idea – convolution masks to reveal certain pattern (points or certainly oriented lines in this case). Examples will be presented on demand on the blackboard 

  5. Convolution mask operators in edge detection • first-order derivatives (backward- and forward-difference, their average) – base for: • Prewitt operators • Sobel operators • Roberts (forward-looking, memory saving )

  6. The Laplacian • advantage – omnodirectional • Drawbacks: • it is a second-order difference operator (thus double edges), figure 5.6 here • no possibility to derive the edge angle • sensitive to noise (no averaging plus amplification of high frequency noise)

  7. The Laplacian of the Gaussian • zero-crossing (not really a definition feature of LoG) • smoothing operator with variance controlling the spatial extent (equation would be nice here ) • Abbreviation LoG (also known as Mexican hat or sombrero) • Gaussian is a lowpass filter, and LoG – a bandpass filter (again, figure 5.7 and 5.8) • Approximation of the LoG – DoG (difference-of-Gaussians), can anyone explain this to me ?

  8. Scale-space methods (multiscale edge detection) • List of names: • Marr-Hildreth spatial coincidence assumption • Witkin’s 1D stability analysis • Liiu et al. (stability maps, discard the first derivative minima as false boundaries) Fully comprehend was beyond my capabilities 

  9. Canny’s method Three criteria relate to: • low probabilites of false edge detection and missing real edges (in the form of an SNR) • good localization (RMS distance of the dtected edge from the true edge) • a single output for single edge Approximation – first derivative of the Gaussian LoG is nondirectional, whereas Canny’s selectively evaluates a directional derivative across each edge (avoiding derivatives that would not contribute to the edge detection but to noise)

  10. Example of Canny’s method

  11. Fourier domain methods • Bandpass filters (the loG filter is a nice example) • Other edge and line detection methods to be discussed in the coming chapters, e.g. Gabor filters and fan filters

  12. Edge linking • Two criteria of similarity of edge pixels: • the strength of the gradient • the direction of the gradient Further proccessing: linking edges separated by small breaks and deleting short isolated segments

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