The first stage of the Canny edge detector involves smoothing with what?

a Gaussian

- a Laplacean mask
- a Sobel mask
- a 3 x 3 blur mask

What information does the Canny edge detector find about each piece of edge?

- the direction
- the magnitude
the magnitude and direction

- the magnitude, direction and width

What is characteristic that the Moravec corner detector uses to identify corners?

- it finds saddle points in the gradient of a region
it finds maxima in the minimum gradient of a region

- it finds regions where the gradients in all directions are almost the same
- it finds minima in the maximum gradient of a region

The Canny edge detector represents more or less the state of the art in detecting edges in images. Why is its output still of limited use?

edges are of limited value in practice

- it doesn't find all edges
- the edges are still too noisy to be useful
- it is too slow

What is the underlying principle of the Moravec corner detector?

moving away from a corner introduces a large change

- moving away from a uniform region introduces a large change
- moving along an edge introduces a large change
- moving away from a corner introduces a small change

Why does the Canny edge detector perform non-maximum suppression?

- to quantise the direction of the edge
- to stop the edges being maxima
- to stop the edges being too bright
to make each edge only one pixel wide

Which of the following is

*not*an underlying principle of the Canny edge detector?- it should respond only to edges
- all edges should be found
a broad edge should produce a response at both sides

- edges should be located in the correct place

Which of the following transformations is not handled by SIFT?

- contrast changes
- scale changes
- rotations
reflections

Why does the last stage of the Canny edge detector employ two thresholds?

not all edges produce a response above both thresholds

- to reduce the amount of noise in an image
- not all edges produce a response between both thresholds
- not all edges produce a response below both thresholds

Which of the following is suitable for matching SIFT features P and Q?

- the expression sqrt{ P(x,y)^2 + Q(x, y)^2 }
the expression sqrt{ (P(x,y) - Q(x, y))^2}

- the expression P(x,y) x Q(x, y)
- the expression sqrt{ P(x,y) } - sqrt{ Q(x, y) }