ME 5405 Computer Project

ME 5405 Machine Vision

CA – AY21/22 Semester 1

Computing Project

You will be assigned to a random group of 2-3 students to work on the computing project.
The software must be developed using MATLAB. You are encouraged to rely on your
own implementations for the different steps of this project. You will be rewarded doing
so. Otherwise, you are allowed to use MATLAB’s Imaging Toolbox.

Images 1 and 2 are 64x64, 32 level images. These images are shown as coded arrays that
contains an alphanumeric character for each pixel. The range of these characters is 0-9
and A-V, which corresponds to 32 levels of gray.

In your report, you should include the followings for Image 1 and Image 2.
1. an introduction to the problem,
2. a description of your algorithm and flow chart,
3. screen captures of every stage of the image processing, and
4. a discussion and conclusion including an explanation on why you choose the method
employed in your project, which investigation you performed, and what lessons you
learned.

You should upload your report and software to LumiNUS-ME5405-Files-Student
Submission by 19 November 2021 (Friday).

This is a group project. Please submit only one set of report and software. All members of
the group will receive the same score. Name your submission as Group XX.zip. Please
indicate the names and IDs of all group members on the cover page of your report.


Image 1: Chromosomes (Available on LumiNUS-ME5405-Files-Lecture –
chromo.txt)

Perform the following tasks:
1. Display the original image on screen.
2. Threshold the image and convert it into binary image.
3. Determine an one-pixel thin image of the objects.
4. Determine the outline(s).
5. Label the different objects.

ME 5405 Computer Project

Image 1

EJNPPRSTSTSTTTURQSVVVVUUUVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV
CFJMNNNNNOPORPQOPORRSQQRQQTSRTSTTTUUTURTUVVVVVVVVVVVUVVVVVVVVVVV
BEILKMMLMLONONPNNNNPOOOPOPTURSQRRRSUTTRSSSSTTSUTVUVUUVVVVVVVVVVV
BFHKLMOMNONNOOOPNMNNOPPOMPORRQQQPRSTSRPSQPTSQTSRTSSUUVUUVVVVVVVV
AEHKLLMNNMMMOPPONNNOOONPONQPQPPQQRRTSTRQRSTSSSUTTTVVUVVVVVVVVVVV
BEGILLMNNMMMPOPONNNNOPPOMOPQRQQPRRSRSRSQPRTTTSSUUSTUVVVUVVVVVVVV
BDHJLMMNOMNOMOOOOLNMOPOONOQORRQPPQSTURQQRQSSTSTTTTTTVUUVVVVVVVVV
BEHKKMNOONNMOMNNMLMLMNQNNOOQQQROPPSTRSRQQQSQTTTTTTTSUVVVVVVVVVVU
CFHJKKMNNNMNLLHKMNLGAPONNNPQQPPOOPQRSQQRQRQTTVTTTRTSUVVVVVVVVVQH
DFIKLMMMONMMIE9DLLLE6ALNOMPQRQPPOPQSRRQOQPRTTUTSURSTUTUVVVVVVSNK
DFIKKMMMMMMLF86CLNLC69KNNMPRRPPOOPSTRSQRPRRSUSSRSRSTUUUVVVVVVUUU
CGJKKLMNMMLKE46FMMLF67JNLOOPRRPPOQRSRSRPQRTTTTTTTTSUUUVVVVVVVVVV
DFHKKLMMNNLID67HOMMI87EOONQPSRPOPQQUURRPORRTTTRRTTTUTUVVVVVVVVVV
DFIJKMNNNMLJC5ALOMNKC9GNPPPRRRRPQQRTTRRQQSSSTSSQRTTSVUVVVVVVVVVV
DFIKMMOOOOMKE49KONNK97HPQPQTUSQQPRSTSRRRRSUUUTRSSSUTVVVVVVVVVVVV
DGJLMMNNOOMKE69JNNMI63GPOPRRTTRQQSTUTSRQRRTTUVTTTTUTVVVVVVVVVVVV
EGJLMNNOONOLD45HMOMJA6FQRQRSUTTRRRVTSTRRRTTUTTTSSTUTVVVVVVVVVVVV
DGJLLLOOONMNH64BLMMLD9IPQQRSTTRRRTVTTRSTTSUVUTSTTRUVVVVVVVVVVVVV
EHKLNMNONOMMLF69KMLKE7HQQQSSUUSSTUVVTSTRTTVVVUTTUUTUVVVVVRVVVVVV
EHJLLOOOPPOONJCAINNKG6CPPQRSUTRSUVVVUSRTSVVUVTRRTTTUVVVVVVVVVVVV
EIJLMOPPOOOMOMJDGMMGA8FNQRSVUTTRSUUUTTTTUVVVUUTUTTTVUVVVVVVVVVVV
EHKMMNOPPONMNMHADMME47HMOOSRUTUTSVVVUTSTUVVVVURTTUVVVVVVVVVVVVVV
EHLMNOOOPPONNOH79JMG66IPNPRTVURSTUVURTSTUVVVVURSUUVVVVVVVVVVVVVV
FHKLMNOPPNNOPPK65GME6EOQOPSTTRQQTVVVTTRSVVVUVTRRTUTVVVVVVVVVVVVV
FHKMNOPOPPONNOOE6EMD9KPROOTTUSQQSVVUTRRTVVVVTSRRRSUVVUUUVVVVVVVV
EHKMNNPPOOOOMOOL85GGENQQPQTTSSQQTVVUSRQSVVUUSSRRTTTUVVVVVVUVVVVV
EHKMNPOPOQPOPPPOI9BEGPRQQRUTTSRQTUVTQRRTTVTURRSRRUTVVUUTUVUVVVVV
FIKMNPQQQPOOOPQNNGBBDLPRRTTTTRRTVVVTRRRUVVTQRRRRTTTVVRORTVVVVVVV
GILNOQRQQPPPQPPPLE8FF9CKQTVUSRRUVVVSQRRUVVURRRTSVSVTRF7ITTUVVVVV
GJLOPQQRPPPPQQQNJ84FK74BOUVUURTVVVVTRQUVVUTSRRTUQLMSQA2ERTUVVVVV
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GJLOPQQPPQQRQRPE26LTQNE59PRSRSVVVVUTVVVVVVSVUVVVTJ8BMB9NTTTVVVVV
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GJPQRRRRQSRSRQH52DRRQLEBKRSRSVVUTSTTVVVVVVVVVVVVVTMFB5BE739EF857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 5405 Computer Project

Image 2: Characters (Available on LumiNUS-ME5405-Files-Lecture – charact1.txt)

Perform the following tasks:

1. Display the original image on screen.
2. Create a binary image using thresholding.
3. Determine a one-pixel thin image of the characters.
4. Determine the outline(s) of characters of the image.
5. Segment the image to separate and label the different characters.
6. Arrange the characters in one line with the sequence: AB123C
7. Rotate the output image from Step 6 about its center by 30 degrees.
8. Using the training dataset provided on LumiNUS (p_dataset_26.zip), train the
(conventional) classification method of your choice (i.e., self-ordered maps (SOM),
k-nearest neighbors (kNN), or support vector machine (SVM)) to recognize the 6
characters (“1”, “2”, “3”, “A”, “B”, or “C”). You should use 75% of the dataset to
train your classifier, and the remaining 25% for validation (testing). Then, test your
trained classifier on each characters in image 2, reporting the final classification
results. Do not use the characters in image 2 as training data for your classifier.
9. Throughout step 8 (training of the classifier), also experiment with pre-processing of
the data (e.g., padding/resizing input images) as well as with hyperparameter tuning.
In your report, discuss how sensitive your approach is to these changes.




ME 5405 Computer Project

Image 2


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