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Due     April 4, 2025 by 11:59pm     Points     100

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In this part you should have your data preprocessing, segmentation and feature extraction implemented. Some customized projects may not follow this standard pipeline, so remember to discuss with Walter what are the appropriate deliverables for you in this phase.

What to do and deliver?

1. A report (no page limit, but try to be concise; 1000-2000 words should suffice) as a separate "Part 3" section of the readme.md in your GitHub repository that includes:
• A list of the methods already applied for data pre-processing and feature extraction (10 points)
• A justification for why you decided to use these algorithms (30 points). For instance, if you used Canny edge detection and the Hough transform to detect lines, say why you believe this feature extraction is good for your project.
• A few illustrations demonstrating how your methods processed the training data, for instance show a few segmentation results (20 points).
• For teams: explain individual contributions of each team member (this is needed to have this assignment graded).
2. Push your current code to your project repository (40 points). This code should implement what you described in the report. Provide instructions how to run your code on a data example (attach this example to the code). Either Walter or Louisa will run them to see how the current solution works.