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 64×64, 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 FJLNPPPPQPPQRQPLA11EJA25LUUUUTVVVVVUQSVVVUVRSTUTP56NRD2DQUUVVVVV GHLOPRPPQPQQRPOH717LNE309PVSSSVVVVVSUUVVVUTTTUUUO32LSF29QVUVVVVV FJMPPQQPPPQQRQPF47JQQMA25PTVSSVVVVVVUVVVVTSUVTVVQD7IQD5EQVVVVVVV GJLOPQQPPQQRQRPE26LTQNE59PRSRSVVVVUTVVVVVVSVUVVVTJ8BMB9NTTTVVVVV GJNQPRRRQRRRRPM927OTQMF7BQRQRTVVVVSTVVVVVVUVVVVVVN68E6BNLHHLQLLH GJPQRRRRQSRSRQH52DRRQLEBKRSRSVVUTSTTVVVVVVVVVVVVVTMFB5BE739EF857 HKOQRPQRSSSRQPI43GSQQH69NSSSUUVVTTTUVVVVUSVVVVVVVVRMGHHHIHOQLD8G HKOQQQSSTSTRRQOHEMSQQNHKTUTSVVVRVTUVVVVTTUUVVVUVVTQEERUUVVVVVTTV ILPQRRSTTTTTSRRRRSTRRTTUUUUVVVVUTTVVVVVSSUUVVVVTQRJ9ERVVVVVVVVVV IMPPRRSTTTTTTTTTSTSTTVVVUTUVVVVVUUVVVUTRTVVVVUTSRK55LVUVVVVVVVVV IMPRTSSUTTTSTTUTTSSTTVVTTVVVVVVVVVVVVURUVVVVUTRRRK47QVVUVVVVVVVV JNPQTTUUUTTTTVTVUTUUVVUTTUVVVVVVVVVVVVVVVVVVVTSTTK8CRVVUTVVVVVVV KOPRTTUVUUTTTVVUUTTVVVUTUVVVVUVVVVVVVVVVVVVVVTUVUO57NVSTTUVVVVVV KNQTUUVVVVUVVVVUTTUVVUTTUUVVVVVVVVVVVVVVVVVVVVVVVP52DOTTUVVVVVVV LNQTVVVVVVVVVVTQPPQTVUTRSTVVUUVVVVVVVVVVVVVVVVVVVVH52BQUUUVVVVVV LPSTVVVVVVVVUSKEDDHMMNKJILPSVVVVVVVVVVVVVVVVVVVVVVSF4DRVVVVVVVVV LPSUVVVVVVVVVO7344343446667FOVVVVVVVVVVVVVVVVVVVVVVQNRVVVVVVVVVV LQTUVVVVVVVVVRB596433369A302BOUVVVVVVVVUSUVVVVVVVVVVVVVVVVVVVVVV MRUVVVVVVVVVVVQOONMLJLPSRH6157ETVVVTLFFDHNSVVVVVVVVVVVVVVVVVVVVR NQUVVVVVVVVVVVVVVVVVUVVVVSLFIEBNVQJGA53458FIOVVVVVVVVVVVVVVVVVVD ORUVVVVVVVVVVVVVVVTQSVVVVVVVVSLKPSNKMMLIB466CPVVVVVVVVVVVVVVVVVA PTUVVVVVVVVVVVURPIEBDJNQOOSQHGKIJUVVVVVVTLJ98KVVVVVVVVVVVVVVVVUG PSVVVVVVVVVVVQJC8435755B87CB47LK9KVVVVVVVVVKISVVVVVVVVVVVVVVVVVO PSVVVVVVVVVVKA85313DIB6A89DEEQVS88NVVVVVVVVVVVVVVVVVVVVVVVVVVVVV QUVVVVVVVVVVMDDFFGLVVUPQSTUVVVVVLCCMUVVVVVVVVVVVVVVVVVVVVVVVVVVV PUVVVVVVVVVVVUTVVVVVVVVVVVVVVVVVVOCBGOVVVVVVVVVVVVVVVVVVVVVVVVVV RUVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVMB79GMOUVVVVVVVVVVVVVVVVVVVVVV SVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVRH9658MVVVVVVVVVVVVVVVVVVVVVV SVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVQIDEQVVVVVVVVVVVVVVVVVVVVVV TVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV TVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV SVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV QUVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV ORUVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV ME 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. 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