CSCI 4261代写、代做 Python，java语言程序

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CSCI 4261 - Introduction to Computer Vision Faculty of Computer Science, Dalhousie University
Practicum 1
Date Given: May 28, 2024 Due Date: May 31, 2024
Plagiarism Policy
• This assignment is an individual task. Collaboration of any type amounts to a violation of the academic integrity policy and will be reported to the AIO.
• Content should not be copied from any source(s). Please understand the concept and write answers in your own words.
• If you wish to learn more Dalhousie Academic Integrity policy , please visit the following link: https://www.dal.ca/dept/university_secretariat/academic-
integrity.html
CSCI 4261 – Introduction to Computer Vision

Assessment Criteria
Task Assessment:
• • • •
100%-90% marks:
The solution you have provided is correct and match all the expected requirements.
90%-80% marks:
The solution is correct but there are areas of improvement or context missing.
80%-70% marks:
The solution is close to the correct answer but your approach is correct.
70% or less marks:
Your solution is not correct and there are obvious loops in your understanding.
Requirements:
For your Practicum 1, you must implement the Canny Edge Detection algorithm from scratch. This means you are only able to use numpy and matplotlib libraries. You can only use OpenCV to apply the smoothing and sharpening.
Canny Edge Detection (100%)
For the image titled “building.jpg”, perform:
1. Canny Edge Detection:
a. Apply the Canny Edge Detection algorithm on building.jpg image.
b. Apply the Canny Edge Detection algorithm on the sharpened building.jpg image (for sharpening the image, follow the same approach of the Task A2 of assignment 2)
2. Share your ideas to improve the algorithm to only find the contour of the image and ignore edges inside the building.jpg image.
Submission Criteria
The submission for this assignment will be done on 2 platforms:
CSCI 4261 – Introduction to Computer Vision

1. Document submission:
The documentation should be a PDF that contain the following:
a. Output to all the practical questions.
b. Answers to the questions asked along with the practical questions. c. At the end mention the link to your Gitlab repository.
Submit the PDF on Brightspace before the deadline.
2. Code submission:
a. Using the repository you created before. (Do not create a new one)
c. Add a new directory named “practicum1”. d. Add your python files containing the code.
Upload you code before the practicum deadline, code pushed after the deadline will not be marked.
You can name the python files according to your preference but the name should clearly indicate the task and subtask they are associated with.
Failure to follow the submission criteria can result into 10% deduction in marks.
CSCI 4261 – Introduction to Computer Vision