UNIVERSITY OF SOUTHAMPTON COMP6223W1
SEMESTER 1 EXAMINATION 2016 - 2017
COMPUTER VISION (MSC)
DURATION 120 MINS (2 Hours)
This paper contains 6 questions
Answer THREE questions.
An outline marking scheme is shown in brackets to the right of each ques-
tion.
University approved calculators MAY be used.
A foreign language dictionary is permitted ONLY IF it is a paper version
of a direct Word to Word translation dictionary AND it contains no notes,
additions or annotations.
7 page examination paper.
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COMP6223W1
Question 1.
(a) Explain what is meant by edge detection in computer vision. De-
scribe the difference in principle between first- and second-order
edge detection.
[9 marks]
(b) Provide a pseudocode description of the Laplacian operator which,
given a grey-level image as input, delivers a binary image where
points are ‘1’ where an edge occurs and ‘0’ otherwise. Explain pre-
cisely how your code should operate, and justify any choices you
have made in your implementation.
[16 marks]
(c) Describe two ways in which the basic Laplacian operator can be
made less sensitive to noise. Discuss the relative advantages and
disadvantages of your approaches.
[8 marks]
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COMP6223W1
Question 2.
(a) Describe the aims and differences between the processes of inten-
sity normalisation and histogram equalisation.
[9 marks]
(b) A monochrome camera is known to have a poor response to low
light and an excessive response to bright illumination. For grey levels
between zero and 127 the gain is 0.5 whereas for grey levels between
128 and 255 the gain is 1.5. Sketch the relationship between camera
output and grey level. Describe using pseudocode an operator that
normalises the output so that the effective camera gain for all grey
levels is 1.0.
[16 marks]
(c) One (rather dated) approach to find image features of interest is to
apply histogram equalisation followed by optimised thresholding. Dis-
cuss advantages and limitations of your new approach developed in
part (b), followed by a form of thresholding, in comparison with the
histogram equalisation based approach.
[8 marks]
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COMP6223W1
Question 3.
(a) Show the bases of the Hough transform for conic sections wherein
the Cartesian parameterisations of a line and of a circle can be viewed
as a parameter space analysis.
[10 marks]
(b) Show how the foot-of-normal parameterisation of a line is derived.
[14 marks]
(c) Describe how the foot-of-normal line parameterisation limits the pa-
rameter ranges leading to a practical implementation of the HT for
lines.
[9 marks]
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COMP6223W1
Question 4.
(a) State the two categories in which shapes (represented by connected
components) can be described. For each category, provide details
of a specific descriptor, and briefly describe how it can be computed
from a connected component.
[8 marks]
(b) Consider the connected component depicted by the solid black pixels
below:
Ensuring you show all working, compute:
• the compactness
• a 4-connected chain-code representation
[11 marks]
(c) Describe in detail the process of creating a Point Distribution Model
to describe the shape of a human face.
[14 marks]
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COMP6223W1
Question 5.
The government of the country of Taghum wants to introduce alpha-
numeric license plates for the owners of motorised vehicles, and roll-out
a state-wide surveillance programme to track vehicles using Automatic
Number Plate Recognition (ANPR) using Computer Vision. You have
been asked to design the system. The government has specified that
it is expecting its license plates to be around the same size as those used
in countries like the UK, and to use some combination of letters from the
English alphabet and digits from standard (Arabic) numerals.
(a) Given that vehicles in Taghum do not currently have license plates,
describe how you would design the licence plates and hardware as-
pects of the computer vision system for performing ANPR. State the
rationale for your design choices.
[15 marks]
(b) Starting with an image captured by your ANPR hardware, describe
in detail the processing that your system will perform to recognise the
individual characters within a licence plate.
[18 marks]
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COMP6223W1
Question 6.
(a) Describe in detail how the Harris and Stephens Corner Detection
algorithm works.
[15 marks]
(b) Given the following Structure Tensor,
1600.0 50.0
50.0 1600.0

assuming k = 0.04, compute the Harris Corner Response, showing
all working. Sketch the type of image patch that this structure tensor
is likely to belong to. [8 marks]
(c) Briefly describe how SIFT features are computed and give details
of a robust method for finding correspondences between two images
using interest points described by SIFT features. [10 marks]
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END OF PAPER
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