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CORK INSTITUTE OF TECHNOLOGY
INSTITIÚID TEICNEOLAÍOCHTA CHORCAÍ
Semester 1 Examinations 2012
Module Title: Image Processing 1
Module Code: ELTR
School: Electrical and Electronic Engineering
Programme Title: Bachelor of Engineering (Honours) in Electronic System Engineering
Programme Code: EELES_8_Y
External Examiner(s): Ian Kennedy, Traoloch O’Brien Internal Examiner(s): Donal O’Donovan
Instructions: Answer ANY 2 questions.
Duration: 2 hours
Sitting: Christmas 2012
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) Discuss 3 factors that affect the choice of lighting solution for particular
applications. [9 %]
(b) Referring to Figure 1.1:
i. State the lighting techniques used in each case and describe a scenario where each would be applied. [9 %]
ii. In each case state whether bright field or dark field is used. Describe the benefits of using bright/dark field illumination. [9 %]
Figure 1. (c) A quality control system is required to verify that the correct surface mount integrated circuit (IC) is inserted at a particular location on a printed circuit board. The IC has dimensions 12 mm 6 mm. The identifier letters on the IC have minimum dimensions of 6 mm high 2 mm wide. Three ICs must be analysed and are contained within a region of 12 cm high 20 cm wide. The vision software requires a minimum of 10 pixels, if the recognition software is to accurately identify the text. i. Calculate an appropriate CCD sensor resolution. [5 %] ii. Determine the lens focal length for this application, if the location of the camera is restricted to a height of 10 cm above the board and the camera range possesses a CCD sensor size of a ( ) – see Figure 1.2. [4 %] iii. Determine the minimum lens resolution necessary to resolve the image. [5 %] [ Continued Over ]
(a) (b) (c)
- (a) Briefly describe the use of each of the following procedures to remove or
alleviate noise problems in an image:
- Local spatial filters
- Morphological operators
- Fourier Transform [9 %]
(b) i. Explain how the concept of convolution operates using Equation 2.1 as a basis. Use diagrams to illustrate your answer. [6 %]
( ) ( ) ∫ ( ) ( ) (Equation 2.1)
ii. Explain how the following C code in Figure 2.1 implements the mechanics of 2D convolution. [4 %] int filterH[3][3] = {1,1,1,0 ,0, 0,-1,-1,-1}; int filterV[3][3] = {-1,0,1,-1 ,0, 1,-1,0,1}; for (row = 1; row< imageRow-1; row++) for (col = 1; col< imageCol-1;col++) { Himage[row][col] = 0; for(filtrow=-1;filtrow<=1;filtrow++) for(filtcol=-1;filtcol<=1;filtcol++) Himage[row][col]+= image[row+filtrow][col+filtcol] *filterH[filtrow+1][filtcol+1]; } Figure 2.
(c) i. Demonstrate the application of 2D convolution by applying the filter [ ] at the locations highlighted in Figure 2.2. Assume the
highlighted pixels represent the centre pixel. [8 %]
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 99 99 99 99 99 99 99 99 99 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Figure 2.
ii. State whether the filter in (c) i is high-pass or low-pass in nature and explain why this is the case. Use the results obtained in (c) i to support your assertion. [6 %]
(d) i. Apply a alpha-trimmed mean filter to the image in Figure 2.3. Pick
distinct and separate locations (see (d) ii) to demonstrate the procedure used to generate the filtered image. [6 %]
ii. Explain the benefit of the filter in (d) i above by comparing its output with that of a averaging filter. [6 %]
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 9 0 0 6 0 0 0 0 9 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 9 1 1 1 9 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 9 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 9 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 9 9 0 1 1 1 1 0 0 0 9 0 0 0 0 0 0 0 0 1 1 9 1 0 0 0 0 0 0 0 0 0 9 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Figure 2.
(e) The wiener filter 𝑚 + (^). / (𝑔 𝑚 ) is an example of an adaptive filter.
Explain why the filter is adaptive. [5 %]
(c) i. Describe the concept of a chain code when used as a boundary descriptor
of an object. [4 %] ii. What are the practical issues associated with a ‘basic’ chain code and how are they corrected? [6 %] iii. Applying the practical corrections discussed in (c) ii, generate a normalised chain code for the object in Figure 3.2. [10 %]
Figure 3.
Appendix A:
Histogram equalisation:
- For an image of grey-levels, compute the image histogram.
- Form the cumulative histogram using:
, - , - , - , - + , -
- Set , - r. , -/
3.1. If ( ) then , - 0. , -/ 1
where ( )^ i.e. the ideal probability distribution value of the output image.
- Re-scan the image and write an output image with grey-levels:
𝑔 [𝑔 ]
Appendix C: (Insert into Examination Booklet)
Name:
Structuring element:
Original Dilation
Erosion Opening
Closing Boundary
0033…
