A system for identifying broken custard cream biscuits looks for rectangular regions that are aligned with the edges of images. Why might this be a bad idea?
If the buscuit is not aligned with the edges, its axis-aligned bounding box will be much larger than an oriented bounding box and this might incorrectly identify the biscuit as being broken -- see figure 5.10 in the lecture notes.
What are grey-level co-occurrence matrices?
GLCMs are scattergrams (2D histograms) computed for two regions of a single image separated by a particular shift. High values in it indicate similarity.
How are GLCMs normally used to identify similar textures?
A number of quantities are computed from the actual GLCMs, with the most common ones being listed in the lecture notes. These are then matched, most often these days using machine learning.
Why is a recursive region labelling algorithm poor in practice?
Recursive implementations of any algorithm save state on the program's stack. Recursive region-labelling algorithms make one recursive call for each pixel in a region, so if a region contains many pixels, stack overflow is likely.
Which is the easiest way to identify broken digestive biscuits on a production line?
Digestive biscuits are normally circular, which means their circularity (the ratio of the square of the circumference to the area) is $4\pi$. All other shapes have a circularity greater than this, which would be the case for a broken biscuit.
An image of a room contains a computer with a display. What feature or features would help you detect the display?
The best approach would be to combine rectangularity (to detect the rectangular feature) and aspect ratio, as computer monitors have a 4:3 or 16:9 aspect ratio. SIFT would be poor and corners do not carry enough information in this context.
A shape descriptor consists of the distance from the middle of a feature in the four compass directions. It is found that one feature produces a perfect match with another when the north of one is aligned with the east of the other. What does this tell you?
Moving the north to the east is a 90-degree clockwise rotation, so this is the best match.
If a histogram has two peaks, where is the best place to put a threshold to separate foreground from background?
In general, the best place to put a threshold is at the bottom of the dip between the two peaks -- this is essentially what Otsu's method does.
Which of the following is a sensible region descriptor?
A region descriptor attempts to encapsulate some characteristic of a region in a number, so anything that does not do this is at best ineffective and at worst just pointless! The location of a region in an image is not a good way of describing its properties, nor is the colour of the pixels outside it. The value of the centre pixel might be a descriptor but in practice it is pretty useless.
In the broken biscuit identifier explored in lectures, what is its most serious problem?
- inadequate testing
non-uniform lighting
- it doesn't identify biscuits that aren't aligned with the image edges
- it doesn't identify overlapping biscuits
The most significant shortcoming is in thresholding the biscuits from the background because of non-uniform lighting, even though your lecturer went to some effort to make it as uniform as possible.