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HU Credits:
3
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
agro informatics
Semester:
1st Semester
Teaching Languages:
Hebrew
Campus:
Rehovot
Course/Module Coordinator:
Dr. Yaron Michael
Coordinator Office Hours:
By appointment
Teaching Staff:
Mr. Yaron Michael,
Mr. yedidya harris
Course/Module description:
Learning of selected topics in image processing and analysis at an introductory level using Python.
These include the following topics: Image digitization, mathematical operations on matrices, color representations, data filtering including Fourier transforms and image segmentation. Students learn to apply the material by implementing and investigating image processing algorithms in Python.
Course/Module aims:
Learning outcomes - On successful completion of this module, students should be able to:
- Proper image acquisition
- What are the prerequisites for a proper image/dataset?
- Read images with Python and perform mathematical operations on them.
- Object detection and image segmentation
- Creating a workflow that allows the extraction of conclusions from experimental images.
Attendance requirements(%):
100%
Teaching arrangement and method of instruction:
Lectures and exercises. In the class exercises, Python will be used to implement the part taught in the lesson. In addition, home exercises will be given in which the students will have to process images according to the methods learned in class.
Course/Module Content:
1. Introduction: applications in agriculture, photography, matrices, useful Python libraries for image processing
2. Basic operations on images
3. Histograms
4. Filters
5. Segmentation and object recognition
6. Area measurement of objects in the image
7. Satellite image processing
8. Machine learning and deep learning tools for image processing
Required Reading:
-
Additional Reading Material:
Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 0 %
Project work 45 %
Assignments 40 %
Reports 0 %
Research project 0 %
Quizzes 15 %
Other 0 %
Additional information:
Composition of the final grade:
15% Midterm exam
40% Exercises (class + home)
45% Final project
The course provides theoretical and practical knowledge in image processing. The learning in the course, both in the classroom and at home, will be in the Python language extensively.
Attendance is mandatory in all classes, and is a condition for submitting the final project.
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