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CORK INSTITUTE OF TECHNOLOGY
INSTITIÚID TEICNEOLAÍOCHTA CHORCAÍ
Semester 1 Examinations 2011
Module Title: Image Processing 1
Module Code: ELTR
School: Electrical and Electronic Engineering
Programme Title: Bachelor of Engineering (Honours) in Electronic Systems Engineering
Programme Code: EELES_8_Y
External Examiner(s): Dr A. Donnellan, Mr I. Kennedy Internal Examiner(s): Mr Donal O’Donovan
Instructions: Answer ANY 2 questions.
Duration: 2 hours
Sitting: Winter 2011
Requirements for this examination:
Note to Candidates: Please check the Programme Title and the Module Title to ensure that you have received the correct examination paper. If in doubt please contact an Invigilator.
- (a) You are required to capture an image of a printed circuit board of dimensions 300 mm 350 mm. The integrated circuit (IC) of interest possesses a minimum lead width of 0.5 mm. The camera must be placed 60 cm above surface of the IC.
i. Using Figure 1.1, determine the minimum sensor resolution required for a CCD. [4 %] ii. Calculate the minimum focal length necessary to display the image. [4 %] ½’’ ( ) CCD 640 x 480 768 x 572 1280 x 1072 2048 x 2048 4000 x 2624 Figure 1.
(b) The 4 important factors that impact on vision illumination are: geometry, structure, colour and filters. Briefly discuss each factor and describe a typical application where each would be used to good effect. [18 %]
(c) Explain the process of dithering when used in the context of printed matter using the Matlab code in Figure 1.2 as a basis for your discussion. Explain the purpose of each line of code. [12 %] x = imread('newborn.jpg'); d = [0 56;84 28]; r = repmat(D,128,128); x = double(x); q = floor(x/85); x4 = q +(x-85q>r); subplot(121); imshow(uint8(85x4)); Figure 1.
(b) i. Briefly describe how edges are characterised in an image and subsequently explain the basic principles of edge detection using 1st^ and 2 nd^ derivatives. [10 %]
ii. Describe the operation of the Canny edge detector and explain why it is less likely than the others to be "fooled" by noise, and more likely to detect true weak edges. [14 %]
(c) Gaussian noise present in an image may be modelled using the formula , - (^) √
( )
. Explain how simulated Gaussian noise may be introduced into an image in practice. For your discussion assume that the noise has a mean value is zero. [8 %]
- (a) Describe the Isodata method for threshold estimation. [10 %]
(b) Describe the Hough transform for detecting a line. Your discussion should include the following: i. The appropriate selection of parametric representation. [3 %] ii. The concept and application of accumulator space. [12 %] iii. How can we look for lines with a certain orientation? [4 %] iv. Any advantages and disadvantages associated with this method. [6 %]
(c) In morphology, Dilation of by , denoted , is defined as: ⋃ *( ) ( ) ( ) ( ) +
i. Give an intuitive explanation of this morphological operator and subsequently explain how ( ) may be used to fill an arbitrary region in a binary image. Explain why the ‘ ’ is required. [7 %]
(continued over)
ii. Using the seed point illustrated in Figure 3.1, apply two iterations of ( ) and briefly explain the result in each case.
Only draw the relevant section of the as is necessary to illustrate your answer. Appendix B contains drawing templates to help you illustrate your answer. [8 %]
Figure 3.
Seed point
Appendix B
Student: Question:
