# A short quiz on convolution and friends

1. If a histogram has two visible peaks, where is the best place to set a threshold to isolate the corresponding regions?

• at the top of the upper peak
• below the lower peak
• mid-way between the peaks

• above the upper peak
2. What is the effect of convolving an image with a +-shaped mask?

• X-shaped features will be enhanced
• dark spots will be enhanced
• light spots will be enhanced
• +-shaped features will be enhanced

3. What is the effect of convolution with a 3 x 3 mask with each coefficient set to 1/9?

• it will detect isolated points
• it will blur edges

• it will enhance edges
• it has no effect
4. What is the effect of convolving an image with a +-shaped mask where the central pixel is -4 and its neighbours to north, south, east and west are 1?

• X-shaped features will be enhanced
• +-shaped features will be enhanced
• the image will be lightened
• dark and light spots will be enhanced

5. The correlation coefficient between two images is a quantity that lies in the range -1 to +1. What does a value of -1 represent?

• one of the images has the same value for every pixel
• there is, on the whole, no similarity between the images
• one of the images is like a photographic negative of the other

• the images are identical
6. What is the effect of convolving an image with the mask $$\pmatrix{0 & 1 & 0\cr 0 & 0 & 0\cr 0 & 0 & 0\cr}$$

• the image is unchanged
• the image is blurred
• the image is shifted down one pixel

• the image is shifted up one pixel
7. What is the effect of convolving an image with the mask $$\pmatrix{0 & 1 & 0\cr 1 & 1 & 1\cr 0 & 1 & 0\cr}$$

• light spots will be enhanced
• dark spots will be enhanced
• +-shaped features will be enhanced

• X-shaped features will be enhanced
8. What is the effect of convolution with the following mask? $$\frac19 \pmatrix{1 & 1 & 1\cr 1 & 1 & 1\cr 1 & 1 & 1\cr}$$

• it will enhance edges
• it has no effect
• it will blur edges

• it will detect isolated points
9. How does the time taken to perform a convolution vary with the dimension of a (square) mask?

• it is proportional to the square of the dimension of the mask

• it is proportional to the logarithm of the dimension of the mask
• it is proportional to the dimension of the mask
• it is not affected by the dimension of the mask
10. Why is simple thresholding not especially effective at locating light features in an image?

• thresholding is a local operation
• thresholds are difficult to determine
• changes in illumination change the lightness of features

• thresholding is a global operation
11.