A quiz on image representation and processing in software

Adrian F. Clark

1. What range of values can be stored in an unsigned byte?

   • 0 -- 256
   • -127 -- +127
   • -128 -- +127

   • 0 -- 255

   An unsigned byte represents 256 distinct values, namely 0, 1,... 255.

2. OpenCV was initially developed in

   • UK
   • Australia

   • Russia

   • USA

   Perhaps surprisingly to some, the early versions of OpenCV were developed in Russia, at Intel's research center in Nizhny Novgorod.

3. Which of the following is not a TV broadcast standard?

   • SECAM
   • PAL
   • NTSC

   • GRL

   NTSC is used in the USA and Japan, SECAM in France and Russia, and PAL in most of the rest of the world. GRL stands for Group of Random Letters.

4. What is plotted along the abscissa (x-axis) of a histogram?

   • standard deviation

   • grey level

   • mean value
   • number of occurrences

   The x-axis of a histogram plots the grey level; the y-axis plots the number of occurrences of that grey level.

5. How does histogram equalization work?

   • histogram bins with low occupancy are doubled

   • histogram bins with low occupancy are spread out

   • histogram bins with low occupancy are merged
   • histogram bins with low occupancy are deleted

   Histogram equalization spreads out low-occupancy parts of a histogram. It is inhereently non-linear in nature and generally a very silly thing to do for computer vision. Your lecture can count on the fingers of one foot the number of times it has helped him develop vision algorithms.

6. Which is the following image formats is least suitable for computer vision?

   • PNG
   • TIFF
   • BMP

   • JPEG

   JPEG is a lossy file format and typically discards high-frequency information, i.e. the detail around edges.

7. When using OpenCV, how do you index a single pixel?

   • image[y][x][c]
   • image[c][y,x]
   • image[y,x][c]

   • image[y,x,c]

   The correct way to index the image is row-column-channel. The first two of these correspond to the way you'd index a matrix, so this is easy to remember. Then you just have to get the syntax right. There is nothing to distinguish a channel from a pixel location as far as numpy is concerned, and you are giving multiple indices into a single data structure -- the answer image[y][x][c] would be one index into three nested data structures. Figure 3.4 in the lecture notes summarizes the representation.

8. If a histogram has most of its values bunched together at its left, how does the image appear?

   • under-exposed

   • well-exposed
   • over-exposed
   • equally-exposed

   If the majority of pixels are are the left of a histogram, they have low values and so the image is dark and that normally means it is under-exposed.

9. After contrast-stretching, an over-exposed image which has only a few grey levels occupied will have a histogram that

   • has all grey-level bins roughly equally occupied
   • has a concentration of large values near high grey-level values

   • tends to show small spikes across the entire range of grey levels

   • has a nice peak of grey levels in its middle region

   A poorly-exposed image (either under- or over-exposed) which is contrast-stretched will normally exhibit only a few occupied bins, and they will be equally-spaced across the entire range of grey levels, giving it a `spiky' appearance.

10. If using histograms to perform content-based image retrieval, which of the following is suitable for determining similarity?

    • the difference

    • the cross-correlation

    • the product
    • the square

    One can use the sum-squared difference but not the difference, product or square. The cross-correlation, however, does determine similarity.