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ME 5405 Machine Vision
创建日期：2024-05-04 14:55:42
标签： ME 5405
Machine Vision

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ME 5405 Computer Project


ME 5405 Machine Vision


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.


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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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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