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 ?

9. 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 

10. 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)

11. Example of Canny’s method

12. 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

13. 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